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Reeves,Pulmonary nodule classification in lung cancer screening with three-dimensional convolutional neural networks,NA,2017,article,NA,NA,NA,NA,NA 10.1371/journal.pone.0219369,https://doi.org/10.1371%2Fjournal.pone.0219369,2019,jul,Public Library of Science ({PLoS}),14,7,e0219369,Wenkai Huang and Yihao Xue and Yu Wu,A {CAD} system for pulmonary nodule prediction based on deep three-dimensional convolutional neural networks and ensemble learning,NA,2019,article,Yuchen Qiu,NA,NA,NA,NA 10.1109/tmi.2016.2535865,https://doi.org/10.1109%2Ftmi.2016.2535865,2016,may,Institute of Electrical and Electronics Engineers ({IEEE}),35,5,1207--1216,Marios Anthimopoulos and Stergios Christodoulidis and Lukas Ebner and Andreas Christe and Stavroula Mougiakakou,Lung Pattern Classification for Interstitial Lung Diseases Using a Deep Convolutional Neural Network,NA,2016,article,NA,NA,NA,NA,NA 10.1016/j.media.2015.08.001,https://doi.org/10.1016%2Fj.media.2015.08.001,2015,dec,Elsevier {BV},26,1,195--202,Francesco Ciompi and Bartjan de Hoop and Sarah J. van Riel and Kaman Chung and Ernst Th. Scholten and Matthijs Oudkerk and Pim A. de Jong and Mathias Prokop and Bram van Ginneken,Automatic classification of pulmonary peri-fissural nodules in computed tomography using an ensemble of 2D views and a convolutional neural network out-of-the-box,NA,2015,article,NA,NA,NA,NA,NA 10.1118/1.4927573,https://doi.org/10.1118%2F1.4927573,2015,aug,Wiley,42,9,5042--5054,Lin Lu and Yongqiang Tan and Lawrence H. Schwartz and Binsheng Zhao,Hybrid detection of lung nodules on {CT} scan images,NA,2015,article,NA,NA,NA,NA,NA 10.1155/2016/6215085,https://doi.org/10.1155%2F2016%2F6215085,2016,NA,Hindawi Limited,2016,NA,1--7,Wei Li and Peng Cao and Dazhe Zhao and Junbo Wang,Pulmonary Nodule Classification with Deep Convolutional Neural Networks on Computed Tomography Images,NA,2016,article,NA,NA,NA,NA,NA 10.1007/s11548-019-01979-1,https://doi.org/10.1007%2Fs11548-019-01979-1,2019,apr,Springer Science and Business Media {LLC},14,11,1969--1979,Li Gong and Shan Jiang and Zhiyong Yang and Guobin Zhang and Lu Wang,Automated pulmonary nodule detection in {CT} images using 3D deep squeeze-and-excitation networks,NA,2019,article,NA,NA,NA,NA,NA 10.1109/tnnls.2019.2892409,https://doi.org/10.1109%2Ftnnls.2019.2892409,2019,nov,Institute of Electrical and Electronics Engineers ({IEEE}),30,11,3484--3495,Fangzhou Liao and Ming Liang and Zhe Li and Xiaolin Hu and Sen Song,Evaluate the Malignancy of Pulmonary Nodules Using the 3-D Deep Leaky Noisy-{OR} Network,NA,2019,article,NA,NA,NA,NA,NA 10.1109/tmi.2016.2536809,https://doi.org/10.1109%2Ftmi.2016.2536809,2016,may,Institute of Electrical and Electronics Engineers ({IEEE}),35,5,1160--1169,Arnaud Arindra Adiyoso Setio and Francesco Ciompi and Geert Litjens and Paul Gerke and Colin Jacobs and Sarah J. van Riel and Mathilde Marie Winkler Wille and Matiullah Naqibullah and Clara I. Sanchez and Bram van Ginneken,Pulmonary Nodule Detection in {CT} Images: False Positive Reduction Using Multi-View Convolutional Networks,NA,2016,article,NA,NA,NA,NA,NA 10.1007/s11548-017-1605-6,https://doi.org/10.1007%2Fs11548-017-1605-6,2017,may,Springer Science and Business Media {LLC},12,10,1799--1808,Aiden Nibali and Zhen He and Dennis Wollersheim,Pulmonary nodule classification with deep residual networks,NA,2017,article,NA,NA,NA,NA,NA 10.1016/j.ejrad.2019.108713,https://doi.org/10.1016%2Fj.ejrad.2019.108713,2019,dec,Elsevier {BV},121,NA,108713,Long Cao and Ruiqiong Shi and Yangyang Ge and Lei Xing and Panli Zuo and Yan Jia and Jie Liu and Yuan He and Xinhao Wang and Shaoliang Luan and Xiangfei Chai and Wei Guo,Fully automatic segmentation of type B aortic dissection from {CTA} images enabled by deep learning,NA,2019,article,NA,NA,NA,NA,NA NA,https://doi.org/10.3233/XST-180490,2019,Sep,IOS Press,27,4,615–629,"Zhao, Xinzhuo and Qi, Shouliang and Zhang, Baihua and Ma, He and Qian, Wei and Yao, Yudong and Sun, Jianjun","Deep CNN models for pulmonary nodule classification: Model modification, model integration, and transfer learning",Journal of X-Ray Science and Technology,Zhao_Qi_Zhang_Ma_Qian_Yao_Sun_2019,article,NA,NL,"08953996, 10959114",10.3233/XST-180490,"BACKGROUND: Deep learning has made spectacular achievements in analysing natural images, but it faces challenges for medical applications partly due to inadequate images. OBJECTIVE: Aiming to classify malignant and benign pulmonary nodules using CT images, we explore different strategies to utilize the state-of-the-art deep convolutional neural networks (CNN). METHODS: Experiments are conducted using the Lung Image Database Consortium image collection (LIDC-IDRI), which is a public database containing 1018 cases. Three strategies are implemented including to 1) modify some state-of-the-art CNN architectures, 2) integrate different CNNs and 3) adopt transfer learning. Totally, 11 deep CNN models are compared using the same dataset. RESULTS: Study demonstrates that, for the model modification scheme, a concise CifarNet performs better than the other modified CNNs with more complex architectures, achieving an area under ROC curve of AUC = 0.90. Integrated CNN models do not significantly improve the classification performance, but the model complexity is reduced. Transfer learning outperforms the other two schemes and ResNet with fine-tuning leads to the best performance with an AUC = 0.94, as well as the sensitivity of 91% and an overall accuracy of 88%. CONCLUSIONS: Model modification, model integration, and transfer learning can play important roles to identify and generate optimal deep CNN models in classifying pulmonary nodules based on CT images efficiently. Transfer learning is preferred when applying deep learning to medical imaging applications." 10.1016/j.media.2017.06.015,https://doi.org/10.1016%2Fj.media.2017.06.015,2017,dec,Elsevier {BV},42,NA,1--13,Arnaud Arindra Adiyoso Setio and Alberto Traverso and Thomas de Bel and Moira S.N. Berens and Cas van den Bogaard and Piergiorgio Cerello and Hao Chen and Qi Dou and Maria Evelina Fantacci and Bram Geurts and Robbert van der Gugten and Pheng Ann Heng and Bart Jansen and Michael M.J. de Kaste and Valentin Kotov and Jack Yu-Hung Lin and Jeroen T.M.C. Manders and Alexander S{\'{o}}{\~{n}}ora-Mengana and Juan Carlos Garc{\'{\i}}a-Naranjo and Evgenia Papavasileiou and Mathias Prokop and Marco Saletta and Cornelia M Schaefer-Prokop and Ernst T. Scholten and Luuk Scholten and Miranda M. Snoeren and Ernesto Lopez Torres and Jef Vandemeulebroucke and Nicole Walasek and Guido C.A. Zuidhof and Bram van Ginneken and Colin Jacobs,"Validation, comparison, and combination of algorithms for automatic detection of pulmonary nodules in computed tomography images: The {LUNA}16 challenge",NA,2017,article,NA,NA,NA,NA,NA 10.1109/tmi.2018.2876510,https://doi.org/10.1109%2Ftmi.2018.2876510,2019,apr,Institute of Electrical and Electronics Engineers ({IEEE}),38,4,991--1004,Yutong Xie and Yong Xia and Jianpeng Zhang and Yang Song and Dagan Feng and Michael Fulham and Weidong Cai,Knowledge-based Collaborative Deep Learning for Benign-Malignant Lung Nodule Classification on Chest {CT},NA,2019,article,NA,NA,NA,NA,NA 10.1016/j.compmedimag.2014.10.001,https://doi.org/10.1016%2Fj.compmedimag.2014.10.001,2015,mar,Elsevier {BV},40,NA,39--48,Ling Ma and Xiabi Liu and Li Song and Chunwu Zhou and Xinming Zhao and Yanfeng Zhao,A new classifier fusion method based on historical and on-line classification reliability for recognizing common {CT} imaging signs of lung diseases,NA,2015,article,NA,NA,NA,NA,NA 10.1016/j.cmpb.2018.05.006,https://doi.org/10.1016%2Fj.cmpb.2018.05.006,2018,aug,Elsevier {BV},162,NA,109--118,Giovanni Lucca Fran{\c{c}}a da Silva and Thales Levi Azevedo Valente and Arist{\'{o}}fanes Corr{\^{e}}a Silva and Anselmo Cardoso de Paiva and Marcelo Gattass,Convolutional neural network-based {PSO} for lung nodule false positive reduction on {CT} images,NA,2018,article,NA,NA,NA,NA,NA 10.1111/1759-7714.12931,https://doi.org/10.1111%2F1759-7714.12931,2018,dec,Wiley,10,2,183--192,Li Li and Zhou Liu and Hua Huang and Meng Lin and Dehong Luo,Evaluating the performance of a deep learning-based computer-aided diagnosis ({DL}-{CAD}) system for detecting and characterizing lung nodules: Comparison with the performance of double reading by radiologists,NA,2018,article,NA,NA,NA,NA,NA 10.1117/12.2255795,https://doi.org/10.1117%2F12.2255795,2017,mar,{SPIE},NA,NA,NA,Sardar Hamidian and Berkman Sahiner and Nicholas Petrick and Aria Pezeshk,3D convolutional neural network for automatic detection of lung nodules in chest {CT},NA,2017,inproceedings,Samuel G. Armato and Nicholas A. Petrick,NA,NA,NA,NA 10.1371/journal.pone.0200721,https://doi.org/10.1371%2Fjournal.pone.0200721,2018,jul,Public Library of Science ({PLoS}),13,7,e0200721,Mizuho Nishio and Osamu Sugiyama and Masahiro Yakami and Syoko Ueno and Takeshi Kubo and Tomohiro Kuroda and Kaori Togashi,"Computer-aided diagnosis of lung nodule classification between benign nodule, primary lung cancer, and metastatic lung cancer at different image size using deep convolutional neural network with transfer learning",NA,2018,article,Yong Deng,NA,NA,NA,NA 10.1016/s2213-2600(18)30286-8,https://doi.org/10.1016%2Fs2213-2600%2818%2930286-8,2018,nov,Elsevier {BV},6,11,837--845,Simon L F Walsh and Lucio Calandriello and Mario Silva and Nicola Sverzellati,Deep learning for classifying fibrotic lung disease on high-resolution computed tomography: a case-cohort study,NA,2018,article,NA,NA,NA,NA,NA 10.1259/bjr.20180334,https://doi.org/10.1259%2Fbjr.20180334,2018,dec,British Institute of Radiology,91,1092,20180334,Jun-Feng Xiong and Tian-Ying Jia and Xiao-Yang Li and Wen Yu and Zhi-Yong Xu and Xu-Wei Cai and Ling Fu and Jie Zhang and Bin-Jie Qin and Xiao-Long Fu and Jun Zhao,Identifying epidermal growth factor receptor mutation status in patients with lung adenocarcinoma by three-dimensional convolutional neural networks,NA,2018,article,NA,NA,NA,NA,NA 10.1016/j.media.2015.03.003,https://doi.org/10.1016%2Fj.media.2015.03.003,2015,may,Elsevier {BV},22,1,102--113,Yang Song and Weidong Cai and Heng Huang and Yun Zhou and Yue Wang and David Dagan Feng,Locality-constrained Subcluster Representation Ensemble for lung image classification,NA,2015,article,NA,NA,NA,NA,NA 10.1007/s11517-018-1850-z,https://doi.org/10.1007%2Fs11517-018-1850-z,2018,jun,Springer Science and Business Media {LLC},56,12,2201--2212,Guanghui Han and Xiabi Liu and Guangyuan Zheng and Murong Wang and Shan Huang,Automatic recognition of 3D {GGO} {CT} imaging signs through the fusion of hybrid resampling and layer-wise fine-tuning {CNNs},NA,2018,article,NA,NA,NA,NA,NA NA,https://doi.org/10.3233/XST-180490,2019,Sep,IOS Press,27,4,615–629,"Zhao, Xinzhuo and Qi, Shouliang and Zhang, Baihua and Ma, He and Qian, Wei and Yao, Yudong and Sun, Jianjun","Deep CNN models for pulmonary nodule classification: Model modification, model integration, and transfer learning",Journal of X-Ray Science and Technology,Zhao_Qi_Zhang_Ma_Qian_Yao_Sun_2019,article,NA,NL,"08953996, 10959114",10.3233/XST-180490,"BACKGROUND: Deep learning has made spectacular achievements in analysing natural images, but it faces challenges for medical applications partly due to inadequate images. OBJECTIVE: Aiming to classify malignant and benign pulmonary nodules using CT images, we explore different strategies to utilize the state-of-the-art deep convolutional neural networks (CNN). METHODS: Experiments are conducted using the Lung Image Database Consortium image collection (LIDC-IDRI), which is a public database containing 1018 cases. Three strategies are implemented including to 1) modify some state-of-the-art CNN architectures, 2) integrate different CNNs and 3) adopt transfer learning. Totally, 11 deep CNN models are compared using the same dataset. RESULTS: Study demonstrates that, for the model modification scheme, a concise CifarNet performs better than the other modified CNNs with more complex architectures, achieving an area under ROC curve of AUC = 0.90. Integrated CNN models do not significantly improve the classification performance, but the model complexity is reduced. Transfer learning outperforms the other two schemes and ResNet with fine-tuning leads to the best performance with an AUC = 0.94, as well as the sensitivity of 91% and an overall accuracy of 88%. CONCLUSIONS: Model modification, model integration, and transfer learning can play important roles to identify and generate optimal deep CNN models in classifying pulmonary nodules based on CT images efficiently. Transfer learning is preferred when applying deep learning to medical imaging applications." 10.1371/journal.pone.0219369,https://doi.org/10.1371%2Fjournal.pone.0219369,2019,jul,Public Library of Science ({PLoS}),14,7,e0219369,Wenkai Huang and Yihao Xue and Yu Wu,A {CAD} system for pulmonary nodule prediction based on deep three-dimensional convolutional neural networks and ensemble learning,NA,2019,article,Yuchen Qiu,NA,NA,NA,NA 10.1186/s12938-018-0529-x,https://doi.org/10.1186%2Fs12938-018-0529-x,2018,jul,Springer Science and Business Media {LLC},17,1,NA,Patrice Monkam and Shouliang Qi and Mingjie Xu and Fangfang Han and Xinzhuo Zhao and Wei Qian,{CNN} models discriminating between pulmonary micro-nodules and non-nodules from {CT} images,NA,2018,article,NA,NA,NA,NA,NA 10.1080/21681163.2015.1124249,https://doi.org/10.1080%2F21681163.2015.1124249,2016,jun,Informa {UK} Limited,6,1,1--6,Mingchen Gao and Ulas Bagci and Le Lu and Aaron Wu and Mario Buty and Hoo-Chang Shin and Holger Roth and Georgios Z. 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Gao and Yu Qian,Prediction of Multidrug-Resistant {TB} from {CT} Pulmonary Images Based on Deep Learning Techniques,NA,2017,article,NA,NA,NA,NA,NA 10.1002/mp.12846,https://doi.org/10.1002%2Fmp.12846,2018,mar,Wiley,45,5,2097--2107,Hongsheng Jin and Zongyao Li and Ruofeng Tong and Lanfen Lin,A deep 3D residual {CNN} for false-positive reduction in pulmonary nodule detection,NA,2018,article,NA,NA,NA,NA,NA 10.1109/tmi.2018.2833385,https://doi.org/10.1109%2Ftmi.2018.2833385,2018,nov,Institute of Electrical and Electronics Engineers ({IEEE}),37,11,2428--2440,Pietro Nardelli and Daniel Jimenez-Carretero and David Bermejo-Pelaez and George R. Washko and Farbod N. Rahaghi and Maria J. Ledesma-Carbayo and Raul San Jose Estepar,Pulmonary Artery{\textendash}Vein Classification in {CT} Images Using Deep Learning,NA,2018,article,NA,NA,NA,NA,NA 10.1093/jamia/ocy098,https://doi.org/10.1093%2Fjamia%2Focy098,2018,aug,Oxford University Press ({OUP}),25,10,1301--1310,Ross Gruetzemacher and Ashish Gupta and David Paradice,3D deep learning for detecting pulmonary nodules in {CT} scans,NA,2018,article,NA,NA,NA,NA,NA 10.1007/s10278-017-0028-9,https://doi.org/10.1007%2Fs10278-017-0028-9,2017,oct,Springer Science and Business Media {LLC},31,4,415--424,Guk Bae Kim and Kyu-Hwan Jung and Yeha Lee and Hyun-Jun Kim and Namkug Kim and Sanghoon Jun and Joon Beom Seo and David A. Lynch,Comparison of Shallow and Deep Learning Methods on Classifying the Regional Pattern of Diffuse Lung Disease,NA,2017,article,NA,NA,NA,NA,NA 10.1109/jbhi.2017.2725903,https://doi.org/10.1109%2Fjbhi.2017.2725903,2018,jul,Institute of Electrical and Electronics Engineers ({IEEE}),22,4,1227--1237,Hongyang Jiang and He Ma and Wei Qian and Mengdi Gao and Yan Li,An Automatic Detection System of Lung Nodule Based on Multigroup Patch-Based Deep Learning Network,NA,2018,article,NA,NA,NA,NA,NA 10.1038/s41598-018-27569-w,https://doi.org/10.1038%2Fs41598-018-27569-w,2018,jun,Springer Science and Business Media {LLC},8,1,NA,Jason L. Causey and Junyu Zhang and Shiqian Ma and Bo Jiang and Jake A. Qualls and David G. Politte and Fred Prior and Shuzhong Zhang and Xiuzhen Huang,Highly accurate model for prediction of lung nodule malignancy with {CT} scans,NA,2018,article,NA,NA,NA,NA,NA 10.3389/fonc.2018.00108,https://doi.org/10.3389%2Ffonc.2018.00108,2018,apr,Frontiers Media {SA},8,NA,NA,Issa Ali and Gregory R. Hart and Gowthaman Gunabushanam and Ying Liang and Wazir Muhammad and Bradley Nartowt and Michael Kane and Xiaomei Ma and Jun Deng,Lung Nodule Detection via Deep Reinforcement Learning,NA,2018,article,NA,NA,NA,NA,NA 10.1016/j.compmedimag.2019.02.003,https://doi.org/10.1016%2Fj.compmedimag.2019.02.003,2019,jun,Elsevier {BV},74,NA,25--36,Xia Huang and Wenqing Sun and Tzu-Liang (Bill) Tseng and Chunqiang Li and Wei Qian,Fast and fully-automated detection and segmentation of pulmonary nodules in thoracic {CT} scans using deep convolutional neural networks,NA,2019,article,NA,NA,NA,NA,NA 10.1016/j.media.2015.02.002,https://doi.org/10.1016%2Fj.media.2015.02.002,2015,may,Elsevier {BV},22,1,48--62,Temesguen Messay and Russell C. Hardie and Timothy R. Tuinstra,Segmentation of pulmonary nodules in computed tomography using a regression neural network approach and its application to the Lung Image Database Consortium and Image Database Resource Initiative dataset,NA,2015,article,NA,NA,NA,NA,NA 10.2147/ott.s80733,https://doi.org/10.2147%2Fott.s80733,2015,aug,Informa {UK} Limited,NA,NA,2015,Yu-Jen Yu-Jen Chen and Kai-Lung Hua and Che-Hao Hsu and Wen-Huang Cheng and Shintami Chusnul Hidayati,Computer-aided classification of lung nodules on computed tomography images via deep learning technique,NA,2015,article,NA,NA,NA,NA,NA 10.1109/embc.2017.8037182,https://doi.org/10.1109%2Fembc.2017.8037182,2017,jul,{IEEE},NA,NA,NA,Shuo Wang and Mu Zhou and Olivier Gevaert and Zhenchao Tang and Di Dong and Zhenyu Liu and Tian Jie,A multi-view deep convolutional neural networks for lung nodule segmentation,NA,2017,inproceedings,NA,NA,NA,NA,NA 10.1109/jbhi.2018.2818620,https://doi.org/10.1109%2Fjbhi.2018.2818620,2019,mar,Institute of Electrical and Electronics Engineers ({IEEE}),23,2,714--722,Marios Anthimopoulos and Stergios Christodoulidis and Lukas Ebner and Thomas Geiser and Andreas Christe and Stavroula Mougiakakou,Semantic Segmentation of Pathological Lung Tissue With Dilated Fully Convolutional Networks,NA,2019,article,NA,NA,NA,NA,NA 10.1155/2017/8314740,https://doi.org/10.1155%2F2017%2F8314740,2017,NA,Hindawi Limited,2017,NA,1--7,QingZeng Song and Lei Zhao and XingKe Luo and XueChen Dou,Using Deep Learning for Classification of Lung Nodules on Computed Tomography Images,NA,2017,article,NA,NA,NA,NA,NA 10.1109/tbme.2016.2613502,https://doi.org/10.1109%2Ftbme.2016.2613502,2017,jul,Institute of Electrical and Electronics Engineers ({IEEE}),64,7,1558--1567,Qi Dou and Hao Chen and Lequan Yu and Jing Qin and Pheng-Ann Heng,Multilevel Contextual 3-D {CNNs} for False Positive Reduction in Pulmonary Nodule Detection,NA,2017,article,NA,NA,NA,NA,NA 10.1016/j.media.2018.10.006,https://doi.org/10.1016%2Fj.media.2018.10.006,2019,jan,Elsevier {BV},51,NA,13--20,Jihye Yun and Jinkon Park and Donghoon Yu and Jaeyoun Yi and Minho Lee and Hee Jun Park and June-Goo Lee and Joon Beom Seo and Namkug Kim,Improvement of fully automated airway segmentation on volumetric computed tomographic images using a 2.5 dimensional convolutional neural net,NA,2019,article,NA,NA,NA,NA,NA 10.1016/j.media.2016.11.001,https://doi.org/10.1016%2Fj.media.2016.11.001,2017,feb,Elsevier {BV},36,NA,52--60,Jean-Paul Charbonnier and Eva M. van Rikxoort and Arnaud A.A. 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Roth and Mingchen Gao and Le Lu and Ziyue Xu and Isabella Nogues and Jianhua Yao and Daniel Mollura and Ronald M. Summers,"Deep Convolutional Neural Networks for Computer-Aided Detection: {CNN} Architectures, Dataset Characteristics and Transfer Learning",NA,2016,article,NA,NA,NA,NA,NA 10.1109/tmi.2019.2947595,https://doi.org/10.1109%2Ftmi.2019.2947595,2020,may,Institute of Electrical and Electronics Engineers ({IEEE}),39,5,1419--1429,Onur Ozdemir and Rebecca L. Russell and Andrew A. Berlin,A 3D Probabilistic Deep Learning System for Detection and Diagnosis of Lung Cancer Using Low-Dose {CT} Scans,NA,2020,article,NA,NA,NA,NA,NA 10.1016/j.compbiomed.2014.12.008,https://doi.org/10.1016%2Fj.compbiomed.2014.12.008,2015,feb,Elsevier {BV},57,NA,139--149,Shiwen Shen and Alex A.T. 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Bethge and Judith Böven and Lino Morris Sawicki and Norman-Philipp Hoff and Patric Kröpil and Gerald Antoch and Johannes Boos,Performance and clinical impact of machine learning based lung nodule detection using vessel suppression in melanoma patients,NA,2018,article,NA,NA,NA,NA,NA 10.1007/s11548-018-1852-1,https://doi.org/10.1007%2Fs11548-018-1852-1,2018,sep,Springer Science and Business Media {LLC},13,11,1697--1706,Naoki Kamiya and Jing Li and Masanori Kume and Hiroshi Fujita and Dinggang Shen and Guoyan Zheng,Fully automatic segmentation of paraspinal muscles from 3D torso {CT} images via multi-scale iterative random forest classifications,NA,2018,article,NA,NA,NA,NA,NA 10.3171/2018.8.focus18191,https://doi.org/10.3171%2F2018.8.focus18191,2018,nov,Journal of Neurosurgery Publishing Group ({JNSPG}),45,5,E4,Andrew T. Hale and David P. Stonko and Li Wang and Megan K. Strother and Lola B. Chambless,Machine learning analyses can differentiate meningioma grade by features on magnetic resonance imaging,NA,2018,article,NA,NA,NA,NA,NA 10.1016/j.jocd.2018.09.007,https://doi.org/10.1016%2Fj.jocd.2018.09.007,2020,oct,Elsevier {BV},23,4,611--622,Andy K.O. Wong and Eva Szabo and Marta Erlandson and Marshall S. Sussman and Sravani Duggina and Anny Song and Shannon Reitsma and Hana Gillick and Jonathan D. Adachi and Angela M. Cheung,A Valid and Precise Semiautomated Method for Quantifying Intermuscular Fat Intramuscular Fat in Lower Leg Magnetic Resonance Images,NA,2020,article,NA,NA,NA,NA,NA 10.1148/radiol.2018171567,https://doi.org/10.1148%2Fradiol.2018171567,2018,aug,Radiological Society of North America ({RSNA}),288,2,573--581,Liang Chen and Anoma Lalani Carlton Jones and Grant Mair and Rajiv Patel and Anastasia Gontsarova and Jeban Ganesalingam and Nikhil Math and Angela Dawson and Basaam Aweid and David Cohen and Amrish Mehta and Joanna Wardlaw and Daniel Rueckert and Paul Bentley and,Rapid Automated Quantification of Cerebral Leukoaraiosis on {CT} Images: A Multicenter Validation Study,NA,2018,article,NA,NA,NA,NA,NA 10.1016/j.ejro.2019.11.003,https://doi.org/10.1016%2Fj.ejro.2019.11.003,2019,NA,Elsevier {BV},6,NA,343--349,Tarek Smayra and Zahra Charara and Ghassan Sleilaty and Gaelle Boustany and Lina Menassa-Moussa and Georges Halaby,Classification and Regression Tree ({CART}) model of sonographic signs in predicting thyroid nodules malignancy,NA,2019,article,NA,NA,NA,NA,NA 10.1109/tcbb.2018.2835444,https://doi.org/10.1109%2Ftcbb.2018.2835444,2019,nov,Institute of Electrical and Electronics Engineers ({IEEE}),16,6,1794--1801,Yujie Feng and Fan Yang and Xichuan Zhou and Yanli Guo and Fang Tang and Fengbo Ren and Jishun Guo and Shuiwang Ji,A Deep Learning Approach for Targeted Contrast-Enhanced Ultrasound Based Prostate Cancer Detection,NA,2019,article,NA,NA,NA,NA,NA 10.1097/scs.0000000000004901,https://doi.org/10.1097%2Fscs.0000000000004901,2019,NA,Ovid Technologies (Wolters Kluwer Health),30,1,91--95,Soh Nishimoto and Yohei Sotsuka and Kenichiro Kawai and Hisako Ishise and Masao Kakibuchi,"Personal Computer-Based Cephalometric Landmark Detection With Deep Learning, Using Cephalograms on the Internet",NA,2019,article,NA,NA,NA,NA,NA 10.1007/s00392-019-01562-3,https://doi.org/10.1007%2Fs00392-019-01562-3,2019,oct,Springer Science and Business Media {LLC},109,6,735--745,Stefan Baumann and Markus Hirt and U. Joseph Schoepf and Marlon Rutsch and Christian Tesche and Matthias Renker and Joseph W. Golden and Sebastian J. Buss and Tobias Becher and Waldemar Bojara and Christel Weiss and Theano Papavassiliu and Ibrahim Akin and Martin Borggrefe and Stefan O. Schoenberg and Holger Haubenreisser and Daniel Overhoff and Dirk Lossnitzer,Correlation of machine learning computed tomography-based fractional flow reserve with instantaneous wave free ratio to detect hemodynamically significant coronary stenosis,NA,2019,article,NA,NA,NA,NA,NA 10.1109/tmi.2019.2953054,https://doi.org/10.1109%2Ftmi.2019.2953054,2020,may,Institute of Electrical and Electronics Engineers ({IEEE}),39,5,1545--1557,Majd Zreik and Robbert W. van Hamersvelt and Nadieh Khalili and Jelmer M. Wolterink and Michiel Voskuil and Max A. Viergever and Tim Leiner and Ivana Isgum,Deep Learning Analysis of Coronary Arteries in Cardiac {CT} Angiography for Detection of Patients Requiring Invasive Coronary Angiography,NA,2020,article,NA,NA,NA,NA,NA 10.1016/j.nicl.2019.102061,https://doi.org/10.1016%2Fj.nicl.2019.102061,2019,NA,Elsevier {BV},24,NA,102061,Nadieh Khalili and E. Turk and M.J.N.L. Benders and P. Moeskops and N.H.P. Claessens and R. de Heus and A. Franx and N. Wagenaar and J.M.P.J. Breur and M.A. Viergever and I. I{\v{s}}gum,Automatic extraction of the intracranial volume in fetal and neonatal {MR} scans using convolutional neural networks,NA,2019,article,NA,NA,NA,NA,NA 10.1109/embc.2018.8512967,https://doi.org/10.1109%2Fembc.2018.8512967,2018,jul,{IEEE},NA,NA,NA,Negar Farzaneh and S.M. Reza Soroushmehr and Hirenkumar Patel and Alexander Wood and Jonathan Gryak and David Fessell and Kayvan Najarian,Automated Kidney Segmentation for Traumatic Injured Patients through Ensemble Learning and Active Contour Modeling,NA,2018,inproceedings,NA,NA,NA,NA,NA 10.1109/embc.2018.8512182,https://doi.org/10.1109%2Fembc.2018.8512182,2018,jul,{IEEE},NA,NA,NA,Alexander Wood and S. M. Reza Soroushmehr and Negar Farzaneh and David Fessell and Kevin R. Ward and Jonathan Gryak and Delaram Kahrobaei and Kayvan Na,Fully Automated Spleen Localization And Segmentation Using Machine Learning And 3D Active Contours,NA,2018,inproceedings,NA,NA,NA,NA,NA 10.1016/j.nicl.2019.101811,https://doi.org/10.1016%2Fj.nicl.2019.101811,2019,NA,Elsevier {BV},23,NA,101811,Jun Pyo Kim and Jeonghun Kim and Yu Hyun Park and Seong Beom Park and Jin San Lee and Sole Yoo and Eun-Joo Kim and Hee Jin Kim and Duk L. Na and Jesse A. Brown and Samuel N. Lockhart and Sang Won Seo and Joon-Kyung Seong,Machine learning based hierarchical classification of frontotemporal dementia and Alzheimer{\textquotesingle}s disease,NA,2019,article,NA,NA,NA,NA,NA 10.1177/2472630317717049,https://doi.org/10.1177%2F2472630317717049,2017,jun,{SAGE} Publications,23,3,259--268,Y{\`{\i}} Xi{\'{a}}ng J. W{\'{a}}ng and Min Deng and Y{\'{a}}o T. Li and Hua Huang and Jason Chi Shun Leung and Weitian Chen and Pu-Xuan Lu,A Combined Use of Intravoxel Incoherent Motion {MRI} Parameters Can Differentiate Early-Stage Hepatitis-b Fibrotic Livers from Healthy Livers,NA,2017,article,NA,NA,NA,NA,NA 10.1007/s10396-017-0811-8,https://doi.org/10.1007%2Fs10396-017-0811-8,2017,aug,Springer Science and Business Media {LLC},45,2,205--212,Mahsa Lotfollahi and Masoumeh Gity and Jing Yong Ye and A. Mahlooji Far,Segmentation of breast ultrasound images based on active contours using neutrosophic theory,NA,2017,article,NA,NA,NA,NA,NA 10.3389/fonc.2019.00941,https://doi.org/10.3389%2Ffonc.2019.00941,2019,oct,Frontiers Media {SA},9,NA,NA,Matthew D. Blackledge and Jessica M. Winfield and Aisha Miah and Dirk Strauss and Khin Thway and Veronica A. Morgan and David J. Collins and Dow-Mu Koh and Martin O. Leach and Christina Messiou,Supervised Machine-Learning Enables Segmentation and Evaluation of Heterogeneous Post-treatment Changes in Multi-Parametric {MRI} of Soft-Tissue Sarcoma,NA,2019,article,NA,NA,NA,NA,NA 10.1093/eurheartj/ehz565,https://doi.org/10.1093%2Feurheartj%2Fehz565,2019,sep,Oxford University Press ({OUP}),41,3,359--367,Subhi J Al'Aref and Gabriel Maliakal and Gurpreet Singh and Alexander R van Rosendael and Xiaoyue Ma and Zhuoran Xu and Omar Al Hussein Alawamlh and Benjamin Lee and Mohit Pandey and Stephan Achenbach and Mouaz H Al-Mallah and Daniele Andreini and Jeroen J Bax and Daniel S Berman and Matthew J Budoff and Filippo Cademartiri and Tracy Q Callister and Hyuk-Jae Chang and Kavitha Chinnaiyan and Benjamin J W Chow and Ricardo C Cury and Augustin DeLago and Gudrun Feuchtner and Martin Hadamitzky and Joerg Hausleiter and Philipp A Kaufmann and Yong-Jin Kim and Jonathon A Leipsic and Erica Maffei and Hugo Marques and Pedro de Ara{\'{u}}jo Gon{\c{c}}alves and Gianluca Pontone and Gilbert L Raff and Ronen Rubinshtein and Todd C Villines and Heidi Gransar and Yao Lu and Erica C Jones and Jessica M Pe{\~{n}}a and Fay Y Lin and James K Min and Leslee J Shaw,Machine learning of clinical variables and coronary artery calcium scoring for the prediction of obstructive coronary artery disease on coronary computed tomography angiography: analysis from the {CONFIRM} registry,NA,2019,article,NA,NA,NA,NA,NA 10.3390/brainsci9090231,https://doi.org/10.3390%2Fbrainsci9090231,2019,sep,{MDPI} {AG},9,9,231,Attallah and Sharkas and Gadelkarim,Fetal Brain Abnormality Classification from {MRI} Images of Different Gestational Age,NA,2019,article,NA,NA,NA,NA,NA 10.1002/mrm.26806,https://doi.org/10.1002%2Fmrm.26806,2017,jun,Wiley,79,3,1696--1707,Umit Yoruk and Brian A. Hargreaves and Shreyas S. Vasanawala,Automatic renal segmentation for {MR} urography using 3D-{GrabCut} and random forests,NA,2017,article,NA,NA,NA,NA,NA 10.1016/j.jtcvs.2017.08.123,https://doi.org/10.1016%2Fj.jtcvs.2017.08.123,2018,feb,Elsevier {BV},155,2,461--469.e4,Charles M. Wojnarski and Eric E. Roselli and Jay J. Idrees and Yuanjia Zhu and Theresa A. Carnes and Ashley M. Lowry and Patrick H. Collier and Brian Griffin and John Ehrlinger and Eugene H. Blackstone and Lars G. Svensson and Bruce W. Lytle,Machine-learning phenotypic classification of bicuspid aortopathy,NA,2018,article,NA,NA,NA,NA,NA 10.2214/ajr.18.20362,https://doi.org/10.2214%2Fajr.18.20362,2019,feb,American Roentgen Ray Society,212,2,342--350,Phillip M. Cheng and Khoa N. Tran and Gilbert Whang and Tapas K. Tejura,Refining Convolutional Neural Network Detection of Small-Bowel Obstruction in Conventional Radiography,NA,2019,article,NA,NA,NA,NA,NA 10.1002/mp.13361,https://doi.org/10.1002%2Fmp.13361,2019,jan,Wiley,46,2,746--755,Michal Byra and Michael Galperin and Haydee Ojeda-Fournier and Linda Olson and Mary O{\textquotesingle}Boyle and Christopher Comstock and Michael Andre,Breast mass classification in sonography with transfer learning using a deep convolutional neural network and color conversion,NA,2019,article,NA,NA,NA,NA,NA 10.1148/radiol.2018181422,https://doi.org/10.1148%2Fradiol.2018181422,2019,feb,Radiological Society of North America ({RSNA}),290,2,537--544,Jared A. Dunnmon and Darvin Yi and Curtis P. Langlotz and Christopher R{\'{e}} and Daniel L. Rubin and Matthew P. Lungren,Assessment of Convolutional Neural Networks for Automated Classification of Chest Radiographs,NA,2019,article,NA,NA,NA,NA,NA 10.3390/brainsci9090231,https://doi.org/10.3390%2Fbrainsci9090231,2019,sep,{MDPI} {AG},9,9,231,Attallah and Sharkas and Gadelkarim,Fetal Brain Abnormality Classification from {MRI} Images of Different Gestational Age,NA,2019,article,NA,NA,NA,NA,NA 10.1148/radiol.2018180887,https://doi.org/10.1148%2Fradiol.2018180887,2019,feb,Radiological Society of North America ({RSNA}),290,2,514--522,Jarrel C. Y. Seah and Jennifer S. N. Tang and Andy Kitchen and Frank Gaillard and Andrew F. Dixon,Chest Radiographs in Congestive Heart Failure: Visualizing Neural Network Learning,NA,2019,article,NA,NA,NA,NA,NA 10.1002/mp.13300,https://doi.org/10.1002%2Fmp.13300,2018,dec,Wiley,46,2,576--589,Wentao Zhu and Yufang Huang and Liang Zeng and Xuming Chen and Yong Liu and Zhen Qian and Nan Du and Wei Fan and Xiaohui Xie,{AnatomyNet}: Deep learning for fast and fully automated whole-volume segmentation of head and neck anatomy,NA,2018,article,NA,NA,NA,NA,NA 10.1016/j.media.2018.11.009,https://doi.org/10.1016%2Fj.media.2018.11.009,2019,feb,Elsevier {BV},52,NA,109--118,Felix Ambellan and Alexander Tack and Moritz Ehlke and Stefan Zachow,Automated segmentation of knee bone and cartilage combining statistical shape knowledge and convolutional neural networks: Data from the Osteoarthritis Initiative,NA,2019,article,NA,NA,NA,NA,NA 10.1097/cm9.0000000000000095,https://doi.org/10.1097%2Fcm9.0000000000000095,2019,feb,Ovid Technologies (Wolters Kluwer Health),132,4,379--387,Lei Ding and Guang-Wei Liu and Bao-Chun Zhao and Yun-Peng Zhou and Shuai Li and Zheng-Dong Zhang and Yu-Ting Guo and Ai-Qin Li and Yun Lu and Hong-Wei Yao and Wei-Tang Yuan and Gui-Ying Wang and Dian-Liang Zhang and Lei Wang,Artificial intelligence system of faster region-based convolutional neural network surpassing senior radiologists in evaluation of metastatic lymph nodes of rectal cancer,NA,2019,article,NA,NA,NA,NA,NA 10.1016/j.radonc.2018.10.037,https://doi.org/10.1016%2Fj.radonc.2018.10.037,2019,feb,Elsevier {BV},131,NA,101--107,Chuang Wang and Neelam Tyagi and Andreas Rimner and Yu-Chi Hu and Harini Veeraraghavan and Guang Li and Margie Hunt and Gig Mageras and Pengpeng Zhang,Segmenting lung tumors on longitudinal imaging studies via a patient-specific adaptive convolutional neural network,NA,2019,article,NA,NA,NA,NA,NA NA,https://doi.org/10.3233/XST-180460,2019,Sep,IOS Press,27,4,591–603,"Yin, Nan and Shen, Cong and Dong, Fuwen and Wang, Jun and Guo, Youmin and Bai, Lu",Computer-aided identification of interstitial lung disease based on computed tomography,Journal of X-Ray Science and Technology,Yin_Shen_Dong_Wang_Guo_Bai_2019,article,NA,NL,"08953996, 10959114",10.3233/XST-180460,"OBJECTIVE: Identification of interstitial lung disease (ILD) may be difficult in certain cases using pulmonary function tests (PFTs) or subjective radiological analysis. We evaluated the efficacy of quantitative computed tomography (CT) with 3-dimensional (3D) reconstruction for distinguishing ILD patients from healthy controls. MATERIALS AND METHODS: We retrospectively collected chest CT images of 102 ILD patients and 102 healthy matched controls, and measured the following parameters: lung parenchymal volume, emphysema indices low attenuation area LAA910 volume, LAA950 volume, LAA910%, and LAA950%, and mean lung density (MLD) for whole lung, left lung, right lung, and each lobe, respectively. The Mann-Whitney U test was used to compare quantitative CT parameters between groups. Receiver operating characteristic (ROC) curves, Bayesian stepwise discriminant analysis, and deep neural network analysis were used to test the discriminative performance of quantitative CT parameters. Binary logistic regression was performed to identify ILD markers. RESULTS: Total lung volume was lower in ILD patients than controls, while emphysema and MLD values were higher (P < 0.001) except LAA910 volume in right middle lobe. LAA910 volume, LAA950 volume, LAA910%, LAA950%, and MLD accurately distinguished ILD patients from healthy controls (AUC >0.5, P < 0.05), and high MLD was a significant marker for ILD (OR = 1.047, P < 0.05). CONCLUSIONS: This quantitative CT analysis can effectively identify ILD patients, providing an alternative to subjective image analysis and PFTs." 10.1016/j.compbiomed.2019.01.022,https://doi.org/10.1016%2Fj.compbiomed.2019.01.022,2019,mar,Elsevier {BV},106,NA,126--139,Stefan Pszczolkowski and Zhe K. Law and Rebecca G. Gallagher and Dewen Meng and David J. Swienton and Paul S. Morgan and Philip M. Bath and Nikola Sprigg and Rob A. Dineen,Automated segmentation of haematoma and perihaematomal oedema in {MRI} of acute spontaneous intracerebral haemorrhage,NA,2019,article,NA,NA,NA,NA,NA 10.1016/j.acra.2018.08.003,https://doi.org/10.1016%2Fj.acra.2018.08.003,2019,mar,Elsevier {BV},26,3,412--423,"Nicholas J. Tustison and Brian B. Avants and Zixuan Lin and Xue Feng and Nicholas Cullen and Jaime F. Mata and Lucia Flors and James C. Gee and Talissa A. Altes and John P. Mugler, III and Kun Qing",Convolutional Neural Networks with Template-Based Data Augmentation for Functional Lung Image Quantification,NA,2019,article,NA,NA,NA,NA,NA 10.18383/j.tom.2018.00036,https://doi.org/10.18383%2Fj.tom.2018.00036,2019,mar,{MDPI} {AG},5,1,201--208,Eric Wu and Lubomir M. Hadjiiski and Ravi K. Samala and Heang-Ping Chan and Kenny H. Cha and Caleb Richter and Richard H. Cohan and Elaine M. Caoili and Chintana Paramagul and Ajjai Alva and Alon Z. Weizer,Deep Learning Approach for Assessment of Bladder Cancer Treatment Response,NA,2019,article,NA,NA,NA,NA,NA 10.1109/tmi.2018.2872031,https://doi.org/10.1109%2Ftmi.2018.2872031,2019,mar,Institute of Electrical and Electronics Engineers ({IEEE}),38,3,762--774,Seung Yeon Shin and Soochahn Lee and Il Dong Yun and Sun Mi Kim and Kyoung Mu Lee,Joint Weakly and Semi-Supervised Deep Learning for Localization and Classification of Masses in Breast Ultrasound Images,NA,2019,article,NA,NA,NA,NA,NA 10.1007/s11548-018-1899-z,https://doi.org/10.1007%2Fs11548-018-1899-z,2019,jan,Springer Science and Business Media {LLC},14,3,545--561,Dimitrios Damopoulos and Till Dominic Lerch and Florian Schmaranzer and Moritz Tannast and Christophe Ch{\^{e}}nes and Guoyan Zheng and J{\'{e}}r{\^{o}}me Schmid,Segmentation of the proximal femur in radial {MR} scans using a random forest classifier and deformable model registration,NA,2019,article,NA,NA,NA,NA,NA 10.1109/tmi.2018.2870343,https://doi.org/10.1109%2Ftmi.2018.2870343,2019,mar,Institute of Electrical and Electronics Engineers ({IEEE}),38,3,686--696,Ravi K. Samala and Heang-Ping Chan and Lubomir Hadjiiski and Mark A. Helvie and Caleb D. Richter and Kenny H. Cha,Breast Cancer Diagnosis in Digital Breast Tomosynthesis: Effects of Training Sample Size on Multi-Stage Transfer Learning Using Deep Neural Nets,NA,2019,article,NA,NA,NA,NA,NA 10.1016/j.neucom.2019.01.103,https://doi.org/10.1016%2Fj.neucom.2019.01.103,2019,apr,Elsevier {BV},338,NA,34--45,Guotai Wang and Wenqi Li and Michael Aertsen and Jan Deprest and S{\'{e}}bastien Ourselin and Tom Vercauteren,Aleatoric uncertainty estimation with test-time augmentation for medical image segmentation with convolutional neural networks,NA,2019,article,NA,NA,NA,NA,NA 10.1016/j.cmpb.2019.02.006,https://doi.org/10.1016%2Fj.cmpb.2019.02.006,2019,apr,Elsevier {BV},171,NA,27--37,Yoga Dwi Pranata and Kuan-Chung Wang and Jia-Ching Wang and Irwansyah Idram and Jiing-Yih Lai and Jia-Wei Liu and I-Hui Hsieh,Deep learning and {SURF} for automated classification and detection of calcaneus fractures in {CT} images,NA,2019,article,NA,NA,NA,NA,NA 10.3389/fnins.2019.00874,https://doi.org/10.3389%2Ffnins.2019.00874,2019,aug,Frontiers Media {SA},13,NA,NA,Jiahang Xu and Fangyang Jiao and Yechong Huang and Xinzhe Luo and Qian Xu and Ling Li and Xueling Liu and Chuantao Zuo and Ping Wu and Xiahai Zhuang,A Fully Automatic Framework for Parkinson's Disease Diagnosis by Multi-Modality Images,NA,2019,article,NA,NA,NA,NA,NA 10.1167/tvst.8.4.25,https://doi.org/10.1167%2Ftvst.8.4.25,2019,aug,Association for Research in Vision and Ophthalmology ({ARVO}),8,4,25,Guohua Shi and Zhenying Jiang and Guohua Deng and Guangxing Liu and Yuan Zong and Chunhui Jiang and Qian Chen and Yi Lu and Xinhuai Sun,Automatic Classification of Anterior Chamber Angle Using Ultrasound Biomicroscopy and Deep Learning,NA,2019,article,NA,NA,NA,NA,NA 10.1148/radiol.2019194005,https://doi.org/10.1148%2Fradiol.2019194005,2019,apr,Radiological Society of North America ({RSNA}),291,1,272--272,Mauro Annarumma and Samuel J. Withey and Robert J. Bakewell and Emanuele Pesce and Vicky Goh and Giovanni Montana,Automated Triaging of Adult Chest Radiographs with Deep Artificial Neural Networks,NA,2019,article,NA,NA,NA,NA,NA 10.1148/radiol.2019194005,https://doi.org/10.1148%2Fradiol.2019194005,2019,apr,Radiological Society of North America ({RSNA}),291,1,272--272,Mauro Annarumma and Samuel J. Withey and Robert J. Bakewell and Emanuele Pesce and Vicky Goh and Giovanni Montana,Automated Triaging of Adult Chest Radiographs with Deep Artificial Neural Networks,NA,2019,article,NA,NA,NA,NA,NA 10.1016/j.media.2018.12.007,https://doi.org/10.1016%2Fj.media.2018.12.007,2019,apr,Elsevier {BV},53,NA,26--38,Emanuele Pesce and Samuel Joseph Withey and Petros-Pavlos Ypsilantis and Robert Bakewell and Vicky Goh and Giovanni Montana,Learning to detect chest radiographs containing pulmonary lesions using visual attention networks,NA,2019,article,NA,NA,NA,NA,NA 10.1016/j.media.2019.02.002,https://doi.org/10.1016%2Fj.media.2019.02.002,2019,apr,Elsevier {BV},53,NA,104--110,Muhammad Arif and Adriaan Moelker and Theo van Walsum,Automatic needle detection and real-time Bi-planar needle visualization during 3D ultrasound scanning of the liver,NA,2019,article,NA,NA,NA,NA,NA 10.1016/j.wneu.2018.10.084,https://doi.org/10.1016%2Fj.wneu.2018.10.084,2019,apr,Elsevier {BV},124,NA,e10--e16,Jan Vargas and Alejandro Spiotta and Arindram Rano Chatterjee,Initial Experiences with Artificial Neural Networks in the Detection of Computed Tomography Perfusion Deficits,NA,2019,article,NA,NA,NA,NA,NA 10.1109/tpami.2018.2840695,https://doi.org/10.1109%2Ftpami.2018.2840695,2019,jul,Institute of Electrical and Electronics Engineers ({IEEE}),41,7,1559--1572,Guotai Wang and Maria A. Zuluaga and Wenqi Li and Rosalind Pratt and Premal A. Patel and Michael Aertsen and Tom Doel and Anna L. David and Jan Deprest and Sebastien Ourselin and Tom Vercauteren,{DeepIGeoS}: A Deep Interactive Geodesic Framework for Medical Image Segmentation,NA,2019,article,NA,NA,NA,NA,NA 10.1503/jpn.180016,https://doi.org/10.1503%2Fjpn.180016,2019,jul,{CMA} Joule Inc.,44,4,246--250,Nikhil Bhagwat and Jon Pipitone and Aristotle N. Voineskos and M. Mallar Chakravarty and,An artificial neural network model for clinical score prediction in Alzheimer disease using structural neuroimaging measures,NA,2019,article,NA,NA,NA,NA,NA 10.1016/j.euf.2019.04.009,https://doi.org/10.1016%2Fj.euf.2019.04.009,2021,jan,Elsevier {BV},7,1,78--85,Ruud J.G. van Sloun and Rogier R. Wildeboer and Christophe K. Mannaerts and Arnoud W. Postema and Maudy Gayet and Harrie P. Beerlage and Georg Salomon and Hessel Wijkstra and Massimo Mischi,"Deep Learning for Real-time, Automatic, and Scanner-adapted Prostate (Zone) Segmentation of Transrectal Ultrasound, for Example, Magnetic Resonance Imaging{\textendash}transrectal Ultrasound Fusion Prostate Biopsy",NA,2021,article,NA,NA,NA,NA,NA 10.1007/s10278-018-0135-2,https://doi.org/10.1007%2Fs10278-018-0135-2,2018,oct,Springer Science and Business Media {LLC},32,2,283--289,Fuk Hay Tang and Jasmine L.C. Chan and Bill K.L. Chan,Accurate Age Determination for Adolescents Using Magnetic Resonance Imaging of the Hand and Wrist with an Artificial Neural Network-Based Approach,NA,2018,article,NA,NA,NA,NA,NA 10.1007/s00535-018-1514-7,https://doi.org/10.1007%2Fs00535-018-1514-7,2018,oct,Springer Science and Business Media {LLC},54,4,321--329,Ren Togo and Nobutake Yamamichi and Katsuhiro Mabe and Yu Takahashi and Chihiro Takeuchi and Mototsugu Kato and Naoya Sakamoto and Kenta Ishihara and Takahiro Ogawa and Miki Haseyama,Detection of gastritis by a deep convolutional neural network from double-contrast upper gastrointestinal barium X-ray radiography,NA,2018,article,NA,NA,NA,NA,NA 10.1109/tmi.2018.2876796,https://doi.org/10.1109%2Ftmi.2018.2876796,2019,apr,Institute of Electrical and Electronics Engineers ({IEEE}),38,4,1026--1036,Alireza Mehrtash and Mohsen Ghafoorian and Guillaume Pernelle and Alireza Ziaei and Friso G. Heslinga and Kemal Tuncali and Andriy Fedorov and Ron Kikinis and Clare M. Tempany and William M. Wells and Purang Abolmaesumi and Tina Kapur,Automatic Needle Segmentation and Localization in {MRI} With 3-D Convolutional Neural Networks: Application to {MRI}-Targeted Prostate Biopsy,NA,2019,article,NA,NA,NA,NA,NA 10.1016/j.ebiom.2019.04.040,https://doi.org/10.1016%2Fj.ebiom.2019.04.040,2019,may,Elsevier {BV},43,NA,454--459,Jinjin Liu and Haoli Xu and Qian Chen and Tingting Zhang and Wenshuang Sheng and Qun Huang and Jiawen Song and Dingpin Huang and Li Lan and Yanxuan Li and Weijian Chen and Yunjun Yang,Prediction of hematoma expansion in spontaneous intracerebral hemorrhage using support vector machine,NA,2019,article,NA,NA,NA,NA,NA 10.1016/j.euo.2018.08.008,https://doi.org/10.1016%2Fj.euo.2018.08.008,2019,may,Elsevier {BV},2,3,257--264,Matthew Truong and Janet E. Baack Kukreja and Soroush Rais-Bahrami and Nimrod S. Barashi and Bokai Wang and Zachary Nuffer and Ji Hae Park and Khoa Lam and Thomas P. Frye and Jeffrey W. Nix and John V. Thomas and Changyong Feng and Brian F. Chapin and John W. Davis and Gary Hollenberg and Aytekin Oto and Scott E. Eggener and Jean V. Joseph and Eric Weinberg and Edward M. Messing,Multi-institutional Clinical Tool for Predicting High-risk Lesions on 3 Tesla Multiparametric Prostate Magnetic Resonance Imaging,NA,2019,article,NA,NA,NA,NA,NA 10.1016/j.media.2019.02.006,https://doi.org/10.1016%2Fj.media.2019.02.006,2019,may,Elsevier {BV},54,NA,1--9,Mattias P. 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Hadjiiski and Yao Lu,Automated pectoral muscle identification {onMLO}-view mammograms: Comparison of deep neural network to conventional computer vision,NA,2019,article,NA,NA,NA,NA,NA 10.1016/j.cmpb.2019.03.011,https://doi.org/10.1016%2Fj.cmpb.2019.03.011,2019,may,Elsevier {BV},173,NA,87--107,Dildar Hussain and Seung-Moo Han,Computer-aided osteoporosis detection from {DXA} imaging,NA,2019,article,NA,NA,NA,NA,NA 10.1016/j.jacep.2019.02.003,https://doi.org/10.1016%2Fj.jacep.2019.02.003,2019,may,Elsevier {BV},5,5,576--586,James P. Howard and Louis Fisher and Matthew J. Shun-Shin and Daniel Keene and Ahran D. Arnold and Yousif Ahmad and Christopher M. Cook and James C. Moon and Charlotte H. Manisty and Zach I. Whinnett and Graham D. Cole and Daniel Rueckert and Darrel P. 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Manikis and Katerina Nikiforaki and Konstantinos Drevelegas and Manos Constantinides and Antonios Drevelegas and Kostas Marias,Extending 2-D Convolutional Neural Networks to 3-D for Advancing Deep Learning Cancer Classification With Application to {MRI} Liver Tumor Differentiation,NA,2019,article,NA,NA,NA,NA,NA 10.1007/s10916-019-1245-1,https://doi.org/10.1007%2Fs10916-019-1245-1,2019,mar,Springer Science and Business Media {LLC},43,5,NA,Yizhang Jiang and Kaifa Zhao and Kaijian Xia and Jing Xue and Leyuan Zhou and Yang Ding and Pengjiang Qian,A Novel Distributed Multitask Fuzzy Clustering Algorithm for Automatic {MR} Brain Image Segmentation,NA,2019,article,NA,NA,NA,NA,NA 10.1109/jbhi.2018.2852635,https://doi.org/10.1109%2Fjbhi.2018.2852635,2019,may,Institute of Electrical and Electronics Engineers ({IEEE}),23,3,1151--1162,Arunava Chakravarty and Jayanthi Sivaswamy,{RACE}-Net: A Recurrent Neural Network for Biomedical Image Segmentation,NA,2019,article,NA,NA,NA,NA,NA 10.1097/rli.0000000000000544,https://doi.org/10.1097%2Frli.0000000000000544,2019,jun,Ovid Technologies (Wolters Kluwer Health),54,6,325--332,Mehmet U. Dalmi{\c{s}} and Albert Gubern-M{\'{e}}rida and Suzan Vreemann and Peter Bult and Nico Karssemeijer and Ritse Mann and Jonas Teuwen,"Artificial Intelligence{\textendash}Based Classification of Breast Lesions Imaged With a Multiparametric Breast {MRI} Protocol With Ultrafast {DCE}-{MRI}, T2, and {DWI}",NA,2019,article,NA,NA,NA,NA,NA 10.1148/radiol.2019182012,https://doi.org/10.1148%2Fradiol.2019182012,2019,jun,Radiological Society of North America ({RSNA}),291,3,677--686,Li Lin and Qi Dou and Yue-Ming Jin and Guan-Qun Zhou and Yi-Qiang Tang and Wei-Lin Chen and Bao-An Su and Feng Liu and Chang-Juan Tao and Ning Jiang and Jun-Yun Li and Ling-Long Tang and Chuan-Miao Xie and Shao-Min Huang and Jun Ma and Pheng-Ann Heng and Joseph T. S. Wee and Melvin L. K. Chua and Hao Chen and Ying Sun,Deep Learning for Automated Contouring of Primary Tumor Volumes by {MRI} for Nasopharyngeal Carcinoma,NA,2019,article,NA,NA,NA,NA,NA 10.1089/thy.2018.0380,https://doi.org/10.1089%2Fthy.2018.0380,2019,jun,Mary Ann Liebert Inc,29,6,858--867,Bin Zhang and Jie Tian and Shufang Pei and Yubing Chen and Xin He and Yuhao Dong and Lu Zhang and Xiaokai Mo and Wenhui Huang and Shuzhen Cong and Shuixing Zhang,Machine Learning{\textendash}Assisted System for Thyroid Nodule Diagnosis,NA,2019,article,NA,NA,NA,NA,NA 10.3174/ajnr.a6070,https://doi.org/10.3174%2Fajnr.a6070,2019,may,American Society of Neuroradiology ({ASNR}),40,6,1074--1081,G. Fan and H. Liu and Z. Wu and Y. Li and C. Feng and D. Wang and J. Luo and W.M. Wells and S. 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Chetty,Automatic Segmentation of the Prostate on {CT} Images Using Deep Neural Networks ({DNN}),NA,2019,article,NA,NA,NA,NA,NA 10.1016/j.compbiomed.2019.06.001,https://doi.org/10.1016%2Fj.compbiomed.2019.06.001,2019,jul,Elsevier {BV},110,NA,244--253,Manish Kumar Sharma and Mainak Jas and Vikrant Karale and Anup Sadhu and Sudipta Mukhopadhyay,Mammogram segmentation using multi-atlas deformable registration,NA,2019,article,NA,NA,NA,NA,NA 10.1016/j.mri.2019.04.001,https://doi.org/10.1016%2Fj.mri.2019.04.001,2019,jul,Elsevier {BV},60,NA,93--100,Qiang Zhang and Huiyu Qiao and Jiaqi Dou and Binbin Sui and Xihai Zhao and Zhensen Chen and Yishi Wang and Shuo Chen and Mingquan Lin and Bernard Chiu and Chun Yuan and Rui Li and Huijun Chen,Plaque components segmentation in carotid artery on simultaneous non-contrast angiography and intraplaque hemorrhage imaging using machine learning,NA,2019,article,NA,NA,NA,NA,NA 10.1161/strokeaha.119.025373,https://doi.org/10.1161%2Fstrokeaha.119.025373,2019,jul,Ovid Technologies (Wolters Kluwer Health),50,7,1734--1741,Ona Wu and Stefan Winzeck and Anne-Katrin Giese and Brandon L. 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Wallace,Deep Learning to Classify Intraductal Papillary Mucinous Neoplasms Using Magnetic Resonance Imaging,NA,2019,article,NA,NA,NA,NA,NA 10.1016/j.joca.2019.02.800,https://doi.org/10.1016%2Fj.joca.2019.02.800,2019,jul,Elsevier {BV},27,7,1002--1010,V. Pedoia and J. Lee and B. Norman and T.M. Link and S. Majumdar,Diagnosing osteoarthritis from T2 maps using deep learning: an analysis of the entire Osteoarthritis Initiative baseline cohort,NA,2019,article,NA,NA,NA,NA,NA 10.1007/s11517-019-01976-1,https://doi.org/10.1007%2Fs11517-019-01976-1,2019,apr,Springer Science and Business Media {LLC},57,7,1567--1580,Genlang Chen and Jiajian Zhang and Deyun Zhuo and Yuning Pan and Chaoyi Pang,Identification of pulmonary nodules via {CT} images with hierarchical fully convolutional networks,NA,2019,article,NA,NA,NA,NA,NA 10.1016/j.pscychresns.2019.06.001,https://doi.org/10.1016%2Fj.pscychresns.2019.06.001,2019,aug,Elsevier {BV},290,NA,1--4,Bianca Besteher and Christian Gaser and Igor Nenadi{\'{c}},Machine-learning based brain age estimation in major depression showing no evidence of accelerated aging,NA,2019,article,NA,NA,NA,NA,NA 10.1016/j.jalz.2019.02.007,https://doi.org/10.1016%2Fj.jalz.2019.02.007,2019,jun,Wiley,15,8,1059--1070,Hongming Li and Mohamad Habes and David A. Wolk and Yong Fan and,A deep learning model for early prediction of Alzheimer{\textquotesingle}s disease dementia based on hippocampal magnetic resonance imaging data,NA,2019,article,NA,NA,NA,NA,NA 10.1016/j.forsciint.2019.05.011,https://doi.org/10.1016%2Fj.forsciint.2019.05.011,2019,aug,Elsevier {BV},301,NA,6--11,Zulal Oner and Muhammed Kamil Turan and Serkan Oner and Yusuf Secgin and Bunyamin Sahin,Sex estimation using sternum part lenghts by means of artificial neural networks,NA,2019,article,NA,NA,NA,NA,NA 10.1016/j.cmpb.2019.06.005,https://doi.org/10.1016%2Fj.cmpb.2019.06.005,2019,aug,Elsevier {BV},177,NA,285--296,Johnatan Carvalho Souza and Jo{\~{a}}o Ot{\'{a}}vio Bandeira Diniz and Jonnison Lima Ferreira and Giovanni Lucca Fran{\c{c}}a da Silva and Arist{\'{o}}fanes Corr{\^{e}}a Silva and Anselmo Cardoso de Paiva,An automatic method for lung segmentation and reconstruction in chest X-ray using deep neural networks,NA,2019,article,NA,NA,NA,NA,NA 10.1148/radiol.2019182622,https://doi.org/10.1148%2Fradiol.2019182622,2019,aug,Radiological Society of North America ({RSNA}),292,2,331--342,Ayelet Akselrod-Ballin and Michal Chorev and Yoel Shoshan and Adam Spiro and Alon Hazan and Roie Melamed and Ella Barkan and Esma Herzel and Shaked Naor and Ehud Karavani and Gideon Koren and Yaara Goldschmidt and Varda Shalev and Michal Rosen-Zvi and Michal Guindy,Predicting Breast Cancer by Applying Deep Learning to Linked Health Records and Mammograms,NA,2019,article,NA,NA,NA,NA,NA 10.1016/j.ymeth.2019.02.010,https://doi.org/10.1016%2Fj.ymeth.2019.02.010,2019,aug,Elsevier {BV},166,NA,103--111,Ming Fan and Yuanzhe Li and Shuo Zheng and Weijun Peng and Wei Tang and Lihua Li,Computer-aided detection of mass in digital breast tomosynthesis using a faster region-based convolutional neural network,NA,2019,article,NA,NA,NA,NA,NA 10.1016/j.compbiomed.2019.103334,https://doi.org/10.1016%2Fj.compbiomed.2019.103334,2019,aug,Elsevier {BV},111,NA,103334,Yoon-Chul Kim and Khu Rai Kim and Kwanghee Choi and Minwoo Kim and Younjoon Chung and Yeon Hyeon Choe,{EVCMR}: A tool for the quantitative evaluation and visualization of cardiac {MRI} data,NA,2019,article,NA,NA,NA,NA,NA 10.1016/j.cmpb.2019.05.003,https://doi.org/10.1016%2Fj.cmpb.2019.05.003,2019,aug,Elsevier {BV},177,NA,47--56,Marko Rak and Johannes Steffen and Anneke Meyer and Christian Hansen and Klaus{\textendash}Dietz Tönnies,Combining convolutional neural networks and star convex cuts for fast whole spine vertebra segmentation in {MRI},NA,2019,article,NA,NA,NA,NA,NA 10.1016/j.ijmedinf.2019.05.002,https://doi.org/10.1016%2Fj.ijmedinf.2019.05.002,2019,aug,Elsevier {BV},128,NA,53--61,Javier Juan-Albarrac{\'{\i}}n and Elies Fuster-Garcia and Germ{\'{a}}n A. Garc{\'{\i}}a-Ferrando and Juan M. Garc{\'{\i}}a-G{\'{o}}mez,{ONCOhabitats}: A system for glioblastoma heterogeneity assessment through {MRI},NA,2019,article,NA,NA,NA,NA,NA 10.1007/s10916-019-1406-2,https://doi.org/10.1007%2Fs10916-019-1406-2,2019,jul,Springer Science and Business Media {LLC},43,8,NA,Serkan Sava{\c{s}} and Nurettin Topalo{\u{g}}lu and Ömer Kazc{\i} and P{\i}nar Nercis Ko{\c{s}}ar,Classification of Carotid Artery Intima Media Thickness Ultrasound Images with Deep Learning,NA,2019,article,NA,NA,NA,NA,NA 10.1007/s10916-019-1366-6,https://doi.org/10.1007%2Fs10916-019-1366-6,2019,jun,Springer Science and Business Media {LLC},43,8,NA,S. Janardhanaprabhu and V. Malathi,Brain Tumor Detection Using Depth-First Search Tree Segmentation,NA,2019,article,NA,NA,NA,NA,NA 10.1109/tip.2019.2905537,https://doi.org/10.1109%2Ftip.2019.2905537,2019,aug,Institute of Electrical and Electronics Engineers ({IEEE}),28,8,4060--4074,Qian Yu and Yinghuan Shi and Jinquan Sun and Yang Gao and Jianbing Zhu and Yakang Dai,Crossbar-Net: A Novel Convolutional Neural Network for Kidney Tumor Segmentation in {CT} Images,NA,2019,article,NA,NA,NA,NA,NA 10.1109/tmi.2019.2894349,https://doi.org/10.1109%2Ftmi.2019.2894349,2019,aug,Institute of Electrical and Electronics Engineers ({IEEE}),38,8,1777--1787,Sarfaraz Hussein and Pujan Kandel and Candice W. Bolan and Michael B. 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Helbich and Margarita Chevalier and Thomas Mertelmeier and Matthew G. Wallis and Ingvar Andersson and Sophia Zackrisson and Ioannis Sechopoulos and Ritse M. Mann,Can we reduce the workload of mammographic screening by automatic identification of normal exams with artificial intelligence? A feasibility study,NA,2019,article,NA,NA,NA,NA,NA 10.1007/s10549-019-05297-7,https://doi.org/10.1007%2Fs10549-019-05297-7,2019,jun,Springer Science and Business Media {LLC},177,2,419--426,Qiujie Yu and Kuan Huang and Ye Zhu and Xiaodan Chen and Wei Meng,Preliminary results of computer-aided diagnosis for magnetic resonance imaging of solid breast lesions,NA,2019,article,NA,NA,NA,NA,NA 10.1007/s10916-019-1416-0,https://doi.org/10.1007%2Fs10916-019-1416-0,2019,jul,Springer Science and Business Media {LLC},43,9,NA,M. Mohammed Thaha and K. Pradeep Mohan Kumar and B. S. Murugan and S. Dhanasekeran and P. Vijayakarthick and A. 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Michael Davidson and William D. Leslie,Identification of Vertebral Fractures by Convolutional Neural Networks to Predict Nonvertebral and Hip Fractures: A Registry-based Cohort Study of Dual X-ray Absorptiometry,NA,2019,article,NA,NA,NA,NA,NA 10.1007/s10278-019-00193-4,https://doi.org/10.1007%2Fs10278-019-00193-4,2019,mar,Springer Science and Business Media {LLC},32,6,980--986,Peter D. Chang and Tony T. Wong and Michael J. Rasiej,Deep Learning for Detection of Complete Anterior Cruciate Ligament Tear,NA,2019,article,NA,NA,NA,NA,NA 10.1007/s00586-019-06158-z,https://doi.org/10.1007%2Fs00586-019-06158-z,2019,oct,Springer Science and Business Media {LLC},28,12,3026--3034,Shahin Ebrahimi and Laurent Gajny and Claudio Vergari and Elsa D. 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Alan Barber,Automated Measurement of Cerebral Atrophy and Outcome in Endovascular Thrombectomy,NA,2019,article,NA,NA,NA,NA,NA 10.1038/s41598-019-53254-7,https://doi.org/10.1038%2Fs41598-019-53254-7,2019,nov,Springer Science and Business Media {LLC},9,1,NA,Su Yang and Jihoon Kweon and Jae-Hyung Roh and Jae-Hwan Lee and Heejun Kang and Lae-Jeong Park and Dong Jun Kim and Hyeonkyeong Yang and Jaehee Hur and Do-Yoon Kang and Pil Hyung Lee and Jung-Min Ahn and Soo-Jin Kang and Duk-Woo Park and Seung-Whan Lee and Young-Hak Kim and Cheol Whan Lee and Seong-Wook Park and Seung-Jung Park,Deep learning segmentation of major vessels in X-ray coronary angiography,NA,2019,article,NA,NA,NA,NA,NA 10.1186/s41747-019-0118-1,https://doi.org/10.1186%2Fs41747-019-0118-1,2019,sep,Springer Science and Business Media {LLC},3,1,NA,Adam Spandorfer and Cody Branch and Puneet Sharma and Pooyan Sahbaee and U. Joseph Schoepf and James G. Ravenel and John W. Nance,Deep learning to convert unstructured {CT} pulmonary angiography reports into structured reports,NA,2019,article,NA,NA,NA,NA,NA 10.3171/2019.6.spine19463,https://doi.org/10.3171%2F2019.6.spine19463,2019,dec,Journal of Neurosurgery Publishing Group ({JNSPG}),31,6,844--850,Kevin T. Huang and Michael A. Silva and Alfred P. See and Kyle C. Wu and Troy Gallerani and Hasan A. Zaidi and Yi Lu and John H. Chi and Michael W. Groff and Omar M. Arnaout,A computer vision approach to identifying the manufacturer and model of anterior cervical spinal hardware,NA,2019,article,NA,NA,NA,NA,NA 10.1186/s12938-019-0623-8,https://doi.org/10.1186%2Fs12938-019-0623-8,2019,jan,Springer Science and Business Media {LLC},18,1,NA,Yonggang Shi and Kun Cheng and Zhiwen Liu,Hippocampal subfields segmentation in brain {MR} images using generative adversarial networks,NA,2019,article,NA,NA,NA,NA,NA 10.1186/s12938-018-0619-9,https://doi.org/10.1186%2Fs12938-018-0619-9,2019,jan,Springer Science and Business Media {LLC},18,1,NA,Mingjie Xu and Shouliang Qi and Yong Yue and Yueyang Teng and Lisheng Xu and Yudong Yao and Wei Qian,Segmentation of lung parenchyma in {CT} images using {CNN} trained with the clustering algorithm generated dataset,NA,2019,article,NA,NA,NA,NA,NA 10.1186/s12911-019-0988-4,https://doi.org/10.1186%2Fs12911-019-0988-4,2019,dec,Springer Science and Business Media {LLC},19,S9,NA,Vitoantonio Bevilacqua and Antonio Brunetti and Giacomo Donato Cascarano and Andrea Guerriero and Francesco Pesce and Marco Moschetta and Loreto Gesualdo,A comparison between two semantic deep learning frameworks for the autosomal dominant polycystic kidney disease segmentation based on magnetic resonance images,NA,2019,article,NA,NA,NA,NA,NA 10.1186/s12957-019-1752-z,https://doi.org/10.1186%2Fs12957-019-1752-z,2019,dec,Springer Science and Business Media {LLC},17,1,NA,Shujun Xia and Jiejie Yao and Wei Zhou and Yijie Dong and Shangyan Xu and Jianqiao Zhou and Weiwei Zhan,A computer-aided diagnosing system in the evaluation of thyroid nodules{\textemdash}experience in a specialized thyroid center,NA,2019,article,NA,NA,NA,NA,NA 10.1007/s00586-019-06115-w,https://doi.org/10.1007%2Fs00586-019-06115-w,2019,aug,Springer Science and Business Media {LLC},28,12,3035--3043,Yaling Pan and Qiaoran Chen and Tongtong Chen and Hanqi Wang and Xiaolei Zhu and Zhihui Fang and Yong Lu,Evaluation of a computer-aided method for measuring the Cobb angle on chest X-rays,NA,2019,article,NA,NA,NA,NA,NA 10.1016/j.media.2019.101556,https://doi.org/10.1016%2Fj.media.2019.101556,2019,dec,Elsevier {BV},58,NA,101556,Seung Yeon Shin and Soochahn Lee and Il Dong Yun and Kyoung Mu Lee,Deep vessel segmentation by learning graphical connectivity,NA,2019,article,NA,NA,NA,NA,NA 10.1002/mp.13862,https://doi.org/10.1002%2Fmp.13862,2019,oct,Wiley,46,12,5514--5527,Zhennong Chen and Francisco Contijoch and Andrew Schluchter and Leo Grady and Michiel Schaap and Web Stayman and Jed Pack and Elliot McVeigh,Precise measurement of coronary stenosis diameter with {CCTA} using {CT} number calibration,NA,2019,article,NA,NA,NA,NA,NA 10.1016/j.media.2019.101558,https://doi.org/10.1016%2Fj.media.2019.101558,2019,dec,Elsevier {BV},58,NA,101558,Nooshin Ghavami and Yipeng Hu and Eli Gibson and Ester Bonmati and Mark Emberton and Caroline M. Moore and Dean C. Barratt,Automatic segmentation of prostate {MRI} using convolutional neural networks: Investigating the impact of network architecture on the accuracy of volume measurement and {MRI}-ultrasound registration,NA,2019,article,NA,NA,NA,NA,NA 10.1097/cm9.0000000000000532,https://doi.org/10.1097%2Fcm9.0000000000000532,2019,nov,Ovid Technologies (Wolters Kluwer Health),132,23,2804--2811,Yuan Gao and Zheng-Dong Zhang and Shuo Li and Yu-Ting Guo and Qing-Yao Wu and Shu-Hao Liu and Shu-Jian Yang and Lei Ding and Bao-Chun Zhao and Shuai Li and Yun Lu,Deep neural network-assisted computed tomography diagnosis of metastatic lymph nodes from gastric cancer,NA,2019,article,NA,NA,NA,NA,NA 10.1186/s41747-019-0120-7,https://doi.org/10.1186%2Fs41747-019-0120-7,2019,oct,Springer Science and Business Media {LLC},3,1,NA,Kyle A. Hasenstab and Guilherme Moura Cunha and Atsushi Higaki and Shintaro Ichikawa and Kang Wang and Timo Delgado and Ryan L. Brunsing and Alexandra Schlein and Leornado Kayat Bittencourt and Armin Schwartzman and Katie J. Fowler and Albert Hsiao and Claude B. Sirlin,Fully automated convolutional neural network-based affine algorithm improves liver registration and lesion co-localization on hepatobiliary phase T1-weighted {MR} images,NA,2019,article,NA,NA,NA,NA,NA 10.1038/s41598-019-51832-3,https://doi.org/10.1038%2Fs41598-019-51832-3,2019,oct,Springer Science and Business Media {LLC},9,1,NA,Beomhee Park and Yongwon Cho and Gaeun Lee and Sang Min Lee and Young-Hoon Cho and Eun Sol Lee and Kyung Hee Lee and Joon Beom Seo and Namkug Kim,A Curriculum Learning Strategy to Enhance the Accuracy of Classification of Various Lesions in Chest-{PA} X-ray Screening for Pulmonary Abnormalities,NA,2019,article,NA,NA,NA,NA,NA 10.1038/s41598-019-40710-7,https://doi.org/10.1038%2Fs41598-019-40710-7,2019,mar,Springer Science and Business Media {LLC},9,1,NA,Junyoung Park and Sungwoo Bae and Seongho Seo and Sohyun Park and Ji-In Bang and Jeong Hee Han and Won Woo Lee and Jae Sung Lee,Measurement of Glomerular Filtration Rate using Quantitative {SPECT}/{CT} and Deep-learning-based Kidney Segmentation,NA,2019,article,NA,NA,NA,NA,NA 10.1002/mp.13739,https://doi.org/10.1002%2Fmp.13739,2019,oct,Wiley,46,12,5544--5561,Jiayi Wu and Jingmin Xin and Xiaofeng Yang and Jie Sun and Dongxiang Xu and Nanning Zheng and Chun Yuan,Deep morphology aided diagnosis network for segmentation of carotid artery vessel wall and diagnosis of carotid atherosclerosis on black-blood vessel wall {MRI},NA,2019,article,NA,NA,NA,NA,NA 10.1186/s13195-019-0526-8,https://doi.org/10.1186%2Fs13195-019-0526-8,2019,aug,Springer Science and Business Media {LLC},11,1,NA,Paula M. Petrone and and Adri{\`{a}} Casamitjana and Carles Falcon and Miquel Artigues and Gr{\'{e}}gory Operto and Raffaele Cacciaglia and Jos{\'{e}} Luis Molinuevo and Ver{\'{o}}nica Vilaplana and Juan Domingo Gispert,Prediction of amyloid pathology in cognitively unimpaired individuals using voxel-wise analysis of longitudinal structural brain {MRI},NA,2019,article,NA,NA,NA,NA,NA 10.1007/s11548-019-01929-x,https://doi.org/10.1007%2Fs11548-019-01929-x,2019,mar,Springer Science and Business Media {LLC},14,12,2057--2068,Koyo Nakayama and Atsushi Saito and Elijah Biggs and Marius George Linguraru and Akinobu Shimizu,"Liver segmentation from low-radiation-dose pediatric computed tomography using patient-specific, statistical modeling",NA,2019,article,NA,NA,NA,NA,NA 10.1136/thoraxjnl-2018-212430,https://doi.org/10.1136%2Fthoraxjnl-2018-212430,2019,sep,{BMJ},74,12,1131--1139,Susan K Mathai and Stephen Humphries and Jonathan A Kropski and Timothy S Blackwell and Julia Powers and Avram D Walts and Cheryl Markin and Julia Woodward and Jonathan H Chung and Kevin K Brown and Mark P Steele and James E Loyd and Marvin I Schwarz and Tasha Fingerlin and Ivana V Yang and David A Lynch and David A Schwartz,{MUC}5B variant is associated with visually and quantitatively detected preclinical pulmonary fibrosis,NA,2019,article,NA,NA,NA,NA,NA 10.1016/j.compbiomed.2019.103490,https://doi.org/10.1016%2Fj.compbiomed.2019.103490,2019,dec,Elsevier {BV},115,NA,103490,Muhammed Kamil Turan and Zulal Oner and Yusuf Secgin and Serkan Oner,A trial on artificial neural networks in predicting sex through bone length measurements on the first and fifth phalanges and metatarsals,NA,2019,article,NA,NA,NA,NA,NA 10.1007/s11548-019-02062-5,https://doi.org/10.1007%2Fs11548-019-02062-5,2019,sep,Springer Science and Business Media {LLC},14,12,2069--2081,Masahiro Oda and Holger R. Roth and Takayuki Kitasaka and Kazunari Misawa and Michitaka Fujiwara and Kensaku Mori,Abdominal artery segmentation method from {CT} volumes using fully convolutional neural network,NA,2019,article,NA,NA,NA,NA,NA 10.1503/jpn.180016,https://doi.org/10.1503%2Fjpn.180016,2019,jul,{CMA} Joule Inc.,44,4,246--250,Nikhil Bhagwat and Jon Pipitone and Aristotle N. Voineskos and M. Mallar Chakravarty and,An artificial neural network model for clinical score prediction in Alzheimer disease using structural neuroimaging measures,NA,2019,article,NA,NA,NA,NA,NA 10.1002/mp.13675,https://doi.org/10.1002%2Fmp.13675,2019,jul,Wiley,46,8,3508--3519,Yuankai Huo and James G. Terry and Jiachen Wang and Sangeeta Nair and Thomas A. Lasko and Barry I. Freedman and J. Jeffery Carr and Bennett A. Landman,Fully automatic liver attenuation estimation combing {CNN} segmentation and morphological operations,NA,2019,article,NA,NA,NA,NA,NA 10.1167/tvst.8.4.25,https://doi.org/10.1167%2Ftvst.8.4.25,2019,aug,Association for Research in Vision and Ophthalmology ({ARVO}),8,4,25,Guohua Shi and Zhenying Jiang and Guohua Deng and Guangxing Liu and Yuan Zong and Chunhui Jiang and Qian Chen and Yi Lu and Xinhuai Sun,Automatic Classification of Anterior Chamber Angle Using Ultrasound Biomicroscopy and Deep Learning,NA,2019,article,NA,NA,NA,NA,NA 10.3389/fnins.2019.00874,https://doi.org/10.3389%2Ffnins.2019.00874,2019,aug,Frontiers Media {SA},13,NA,NA,Jiahang Xu and Fangyang Jiao and Yechong Huang and Xinzhe Luo and Qian Xu and Ling Li and Xueling Liu and Chuantao Zuo and Ping Wu and Xiahai Zhuang,A Fully Automatic Framework for Parkinson's Disease Diagnosis by Multi-Modality Images,NA,2019,article,NA,NA,NA,NA,NA NA,https://doi.org/10.3233/XST-180460,2019,Sep,IOS Press,27,4,591–603,"Yin, Nan and Shen, Cong and Dong, Fuwen and Wang, Jun and Guo, Youmin and Bai, Lu",Computer-aided identification of interstitial lung disease based on computed tomography,Journal of X-Ray Science and Technology,Yin_Shen_Dong_Wang_Guo_Bai_2019,article,NA,NL,"08953996, 10959114",10.3233/XST-180460,"OBJECTIVE: Identification of interstitial lung disease (ILD) may be difficult in certain cases using pulmonary function tests (PFTs) or subjective radiological analysis. We evaluated the efficacy of quantitative computed tomography (CT) with 3-dimensional (3D) reconstruction for distinguishing ILD patients from healthy controls. MATERIALS AND METHODS: We retrospectively collected chest CT images of 102 ILD patients and 102 healthy matched controls, and measured the following parameters: lung parenchymal volume, emphysema indices low attenuation area LAA910 volume, LAA950 volume, LAA910%, and LAA950%, and mean lung density (MLD) for whole lung, left lung, right lung, and each lobe, respectively. The Mann-Whitney U test was used to compare quantitative CT parameters between groups. Receiver operating characteristic (ROC) curves, Bayesian stepwise discriminant analysis, and deep neural network analysis were used to test the discriminative performance of quantitative CT parameters. Binary logistic regression was performed to identify ILD markers. RESULTS: Total lung volume was lower in ILD patients than controls, while emphysema and MLD values were higher (P < 0.001) except LAA910 volume in right middle lobe. LAA910 volume, LAA950 volume, LAA910%, LAA950%, and MLD accurately distinguished ILD patients from healthy controls (AUC >0.5, P < 0.05), and high MLD was a significant marker for ILD (OR = 1.047, P < 0.05). CONCLUSIONS: This quantitative CT analysis can effectively identify ILD patients, providing an alternative to subjective image analysis and PFTs." 10.3390/brainsci9090231,https://doi.org/10.3390%2Fbrainsci9090231,2019,sep,{MDPI} {AG},9,9,231,Attallah and Sharkas and Gadelkarim,Fetal Brain Abnormality Classification from {MRI} Images of Different Gestational Age,NA,2019,article,NA,NA,NA,NA,NA 10.1093/eurheartj/ehz565,https://doi.org/10.1093%2Feurheartj%2Fehz565,2019,sep,Oxford University Press ({OUP}),41,3,359--367,Subhi J Al'Aref and Gabriel Maliakal and Gurpreet Singh and Alexander R van Rosendael and Xiaoyue Ma and Zhuoran Xu and Omar Al Hussein Alawamlh and Benjamin Lee and Mohit Pandey and Stephan Achenbach and Mouaz H Al-Mallah and Daniele Andreini and Jeroen J Bax and Daniel S Berman and Matthew J Budoff and Filippo Cademartiri and Tracy Q Callister and Hyuk-Jae Chang and Kavitha Chinnaiyan and Benjamin J W Chow and Ricardo C Cury and Augustin DeLago and Gudrun Feuchtner and Martin Hadamitzky and Joerg Hausleiter and Philipp A Kaufmann and Yong-Jin Kim and Jonathon A Leipsic and Erica Maffei and Hugo Marques and Pedro de Ara{\'{u}}jo Gon{\c{c}}alves and Gianluca Pontone and Gilbert L Raff and Ronen Rubinshtein and Todd C Villines and Heidi Gransar and Yao Lu and Erica C Jones and Jessica M Pe{\~{n}}a and Fay Y Lin and James K Min and Leslee J Shaw,Machine learning of clinical variables and coronary artery calcium scoring for the prediction of obstructive coronary artery disease on coronary computed tomography angiography: analysis from the {CONFIRM} registry,NA,2019,article,NA,NA,NA,NA,NA 10.3389/fonc.2019.00941,https://doi.org/10.3389%2Ffonc.2019.00941,2019,oct,Frontiers Media {SA},9,NA,NA,Matthew D. Blackledge and Jessica M. Winfield and Aisha Miah and Dirk Strauss and Khin Thway and Veronica A. Morgan and David J. Collins and Dow-Mu Koh and Martin O. Leach and Christina Messiou,Supervised Machine-Learning Enables Segmentation and Evaluation of Heterogeneous Post-treatment Changes in Multi-Parametric {MRI} of Soft-Tissue Sarcoma,NA,2019,article,NA,NA,NA,NA,NA 10.1007/s00392-019-01562-3,https://doi.org/10.1007%2Fs00392-019-01562-3,2019,oct,Springer Science and Business Media {LLC},109,6,735--745,Stefan Baumann and Markus Hirt and U. Joseph Schoepf and Marlon Rutsch and Christian Tesche and Matthias Renker and Joseph W. Golden and Sebastian J. Buss and Tobias Becher and Waldemar Bojara and Christel Weiss and Theano Papavassiliu and Ibrahim Akin and Martin Borggrefe and Stefan O. Schoenberg and Holger Haubenreisser and Daniel Overhoff and Dirk Lossnitzer,Correlation of machine learning computed tomography-based fractional flow reserve with instantaneous wave free ratio to detect hemodynamically significant coronary stenosis,NA,2019,article,NA,NA,NA,NA,NA 10.3390/biom9110673,https://doi.org/10.3390%2Fbiom9110673,2019,oct,{MDPI} {AG},9,11,673,Jun Akatsuka and Yoichiro Yamamoto and Tetsuro Sekine and Yasushi Numata and Hiromu Morikawa and Kotaro Tsutsumi and Masato Yanagi and Yuki Endo and Hayato Takeda and Tatsuro Hayashi and Masao Ueki and Gen Tamiya and Ichiro Maeda and Manabu Fukumoto and Akira Shimizu and Toyonori Tsuzuki and Go Kimura and Yukihiro Kondo,Illuminating Clues of Cancer Buried in Prostate {MR} Image: Deep Learning and Expert Approaches,NA,2019,article,NA,NA,NA,NA,NA 10.3390/jcm8111826,https://doi.org/10.3390%2Fjcm8111826,2019,nov,{MDPI} {AG},8,11,1826,Chi-Hung Weng and Chih-Li Wang and Yu-Jui Huang and Yu-Cheng Yeh and Chen-Ju Fu and Chao-Yuan Yeh and Tsung-Ting Tsai,Artificial Intelligence for Automatic Measurement of Sagittal Vertical Axis Using {ResUNet} Framework,NA,2019,article,NA,NA,NA,NA,NA 10.1002/jmri.26983,https://doi.org/10.1002%2Fjmri.26983,2019,nov,Wiley,51,6,1689--1696,Cian M. Scannell and Mitko Veta and Adriana D.M. Villa and Eva C. Sammut and Jack Lee and Marcel Breeuwer and Amedeo Chiribiri,Deep-Learning-Based Preprocessing for Quantitative Myocardial Perfusion {MRI},NA,2019,article,NA,NA,NA,NA,NA 10.1002/jum.15206,https://doi.org/10.1002%2Fjum.15206,2019,dec,Wiley,39,6,1187--1194,Michael Blaivas and Laura Blaivas,Are All Deep Learning Architectures Alike for Point-of-Care Ultrasound?: Evidence From a Cardiac Image Classification Model Suggests Otherwise,NA,2019,article,NA,NA,NA,NA,NA