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

Table 3 Evaluation results of different models on the 2.5-dimensional deep learning-based multi-instance learning method

Dataset	Model	Accuracy	AUC (95%CI)	Sensitivity	Specificity
Training	ExtraTrees	0.821	0.917 (0.874-0.961)	0.784	0.843
Validation	ExtraTrees	0.776	0.861 (0.756-0.966)	0.882	0.732
Training	LightGBM	0.821	0.880 (0.821-0.940)	0.824	0.819
Validation	LightGBM	0.793	0.877 (0.784-0.970)	0.882	0.756
Training	MLP	0.836	0.914 (0.868-0.959)	0.824	0.843
Validation	MLP	0.776	0.841 (0.727-0.956)	0.882	0.732

AUC: Area under the curve; CI: Confidence interval; MLP: Multi-layer perception.