Figure 2.

The proposed deep learning framework (DeepPCR) for pCR prediction. (A) WSIs with tumors annotated by expert pathologists. (B) All WSIs were cropped into small patches with a size of 299×299 pixels at a magnification of 20×. (C) An in-house deep learning-based color normalization method was applied to ensure the color consistency of the cropped patches. (D) Illustration of the proposed DeepPCR model for pCR candidate prediction. Three scales of phenotype feature representations (i.e., patchPR, clusPR, and wsiPR) were integrated to derive the final prediction.