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Fig. 1 | Journal of Translational Medicine

Fig. 1

From: Deep learning-based predictive biomarker of pathological complete response to neoadjuvant chemotherapy from histological images in breast cancer

Fig. 1

The pipeline of the pCR-score computing consists of five sub-steps: a pre-processing; b CNN I; c middle-processing; d CNN II; and e post-processing. First, the pre-processing step segments valid tissue areas from the input WSI and crops the segmented valid tissue areas into small tiles. Second, the CNN I takes the cropped tiles as inputs and identifies TE regions by mapping the input tiles into probabilities corresponding to TE. Third, the middle-processing step selects TE tiles with identified high probabilities from the outputs of CNN I. Fourth, the CNN II takes the selected TE tiles as inputs and score the pCR of the input tiles by mapping them into probabilities corresponding to pCR. Finally, the post-processing step fuses the pCR probabilities of TE tiles scored via CNN II to produce the final predicted pCR-score of the input WSI

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