Computed tomography-based deep learning and multi-instance learning for predicting microvascular invasion and prognosis in hepatocellular carcinoma

Table 2 Slice-level prediction results for various deep learning model

Dataset	Model	Accuracy	AUC (95%CI)	Sensitivity	Specificity
Training	Resnet18	0.771	0.841 (0.820-0.861)	0.701	0.814
Validation	Resnet18	0.757	0.777 (0.739-0.814)	0.705	0.777
Training	VGG19	0.695	0.770 (0.745-0.794)	0.708	0.688
Validation	VGG19	0.790	0.749 (0.708-0.791)	0.472	0.911
Training	Densenet121	0.750	0.845 (0.825-0.866)	0.809	0.714
Validation	Densenet121	0.704	0.645 (0.596-0.694)	0.437	0.805

AUC: Area under the curve; CI: Confidence interval; VGG19: Visual Geometry Group 19.