Fig. 2
From: Predicting Nottingham grade in breast cancer digital pathology using a foundation model
Comprehensive Workflow for Histopathological Analysis. (A) Displays segmentation of histopathological images using the CLAM model, isolating tissue-only tiles and dividing them into 224 × 224 pixel patches. (B) The UNI model with DINO, pretrained using self-supervised learning (SSL), extracts 1,024-dimensional features at the patch level, which are subsequently aggregated within the MIL framework using attention-based selection. (C) The model predicts the Nottingham grade using a combination of multi-branch learning, stochastic top-K instance masking, and attention mechanisms. The results are visualized using survival analysis, heatmap visualization, and gene ontology analysis