Table 2.

Summary of studies applying deep learning to focal liver disease

Study	Task	Data used for deep learning	US system	Total no. of images	No. of validation set images	Ground truth	Method
Liu et al. (2020) [17]	TACE response prediction	CEUS	Three	130 CEUS datasets	41 CEUS cine sets	mRECIST on CT/MRI	3D-CNN
Pan et al. (2019) [15]	Classification	CEUS	N/A	4,420 images from 242 tumors	10-Fold cross-validation	N/A	3D-CNN
Guo et al. (2018) [13]	Classification	CEUS	One	93 CEUS datasets	5-Fold cross-validation	Pathology, CT, MRI	DCCA-MKL
Schmauch et al. (2019) [16]	Detection and classification	B-mode	N/A	544 images	3-Fold cross-validation 177 Images for external validation	N/A	ResNet50
Hassan et al. (2017) [14]	Detection and classification	B-mode	N/A	110 images	10-Fold cross-validation	Unsupervised learning	SSAE+SVM

US, ultrasonography; TACE, transarterial chemoembolization; CEUS, contrast-enhanced ultrasonography; mRECIST, modified Response Evaluation Criteria in Solid Tumor; CT, computed tomography; MRI, magnetic resonance imaging; 3D, three-dimensional; CNN, convolutional neural network; N/A, not available; DCCA-MKL, deep canonical correlation analysis-multiple kernel learning; SSAE, stacked sparse auto-encoders; SVM, support vector machine.