Fig. 1: H&E Molecular neural network (HEMnet) workflow overview. | npj Precision Oncology

Fig. 1: H&E Molecular neural network (HEMnet) workflow overview.

From: A deep learning model for molecular label transfer that enables cancer cell identification from histopathology images

Fig. 1: H&E Molecular neural network (HEMnet) workflow overview.

a Matched p53 IHC stained and H&E-stained WSI derived from two adjacent tissue sections. b Training was performed on paired normal and cancer slides (five pairs). Test slides were held-back and are unseen by the model training. c Preprocessing to account for technical variations in slide preparation through stain normalization and image registration. d Molecular labels were transferred from p53 to H&E images. Post label transferring, each image was tiled to generate thousands of small samples (224 × 224 pixels) to train a CNN. e Application of HEMnet to predict cancer from new clinical H&E images.

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