Deep learning-based multimodal model for predicting on-treatment histological outcomes in chronic hepatitis B-associated advanced liver fibrosis

Figure 2 Workflow of the development and testing of deep learning model. A and B: Digital hematoxylin and eosin (HE)- and Masson-stained slides were first categorized according to whether fibrosis reversal occurred. Whole-slide images were then partitioned into 512 × 512-pixel tiles and subjected to quality control to remove non-informative regions. Color normalization was applied to the HE tiles, whereas Masson-stained tiles were retained in their original color. The quality-controlled tiles, along with their corresponding labels, were used to train convolutional neural networks models; C: Univariate and multivariate logistic regression analyses were performed to identify clinical characteristics associated with histological outcomes; D: A logistic regression fusion model was developed using HE score, Masson score, and clinical score as input variables, followed by performance validation on internal and external validation sets. WSI: Whole-slide image; CNNs: Convolutional neural networks; Grad-CAM: Gradient-weighted Class Activation Mapping; HE: Hematoxylin and eosin; AUC: Area under the receiver operating characteristic curve.