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

Figure 4 Feature selection and receiver operating characteristic curve results in multi-instance learning modeling. A: The distribution of multi-instance feature differences between microvascular invasion (MVI)-positive and MVI-negative groups; B: Least absolute shrinkage and selection operator regression analysis under 10-fold cross-validation; C: Feature weights after dimensionality reduction; D: Receiver operating characteristic (ROC) curves of three models: ExtraTrees (red), multilayer perceptron (MLP) (cyan), and LightGBM (blue) in the training set; E: Shows ROC curves of three models: ExtraTrees (red), MLP (cyan), and LightGBM (blue) in the validation set. PLH: Predictive likelihood histogram; BoW: Bag-of-word; ROC: Receiver operating characteristic; MIL: Multi-instance; AUC: Area under the curve; MLP: Multi-layer perception.