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. 2024 Apr 11;14(5):3501–3518. doi: 10.21037/qims-23-1600

Table 3. Statistics for total citations of references (14,37-52).

Rank References Citations
1 He K, 2016, Conference on Computer Vision and Pattern Recognition (CVPR), Deep residual learning for image recognition 1,602
2 Ronneberger O, 2015, Lecture Notes in Computer Science, U-Net: Convolutional Networks for Biomedical Image Segmentation 1,419
3 Krizhevsky A, 2012, Communications of the ACM, ImageNet classification with deep convolutional neural networks 1,228
4 Karen S, 2015, International Conference on Learning Representations (ICLR), Very Deep Convolutional Networks for Large-Scale Image Recognition 1,103
5 Szegedy C, 2015, Conference on Computer Vision and Pattern Recognition (CVPR), Going deeper with convolutions 984
6 Kingma DP, 2014, arXiv, Adam: A Method for Stochastic Optimization 746
7 Litjens G, 2017, Med Image Anal, A Survey on Deep Learning in Medical Image Analysis 639
8 Shelhamer E, 2015, Conference on Computer Vision and Pattern Recognition (CVPR), Fully Convolutional Networks for Semantic Segmentation 484
9 Milletari F, 2016 Fourth International Conference on 3D Vision (3DV), V-Net: Fully Convolutional Neural Networks for Volumetric Medical Image Segmentation 476
10 Huang G, 2017, Conference on Computer Vision and Pattern Recognition (CVPR), Densely Connected Convolutional Networks 466
11 LeCun Y, 2015, Nature, Deep Learning 398
12 Srivastava N, 2014, Journal of Machine Learning Research, Dropout: A Simple Way to Prevent Neural Networks from Overfitting 379
13 Deng J, 2009, Conference on Computer Vision and Pattern Recognition (CVPR), Imagenet: A large scale hierarchical image database 370
14 LeCun Y, 1998, Proceedings of the IEEE, Gradient-based learning applied to document recognition 363
15 Russakovsky O, 2015, Int J Comput Vis, Imagenet large scale visual recognition challenge 335
16 Shin HC, 2016, IEEE Trans Med Imaging, Deep Convolutional Neural Networks for Computer-Aided Detection: CNN Architectures, Dataset Characteristics and Transfer Learning 312
17 Kamnitsas K, 2017, Med Image Anal, Efficient Multi-Scale 3D CNN with fully connected CRF for Accurate Brain Lesion Segmentation 302