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. 2023 Jun 16;13:1179025. doi: 10.3389/fonc.2023.1179025

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Copyright © 2023 To, Lu, Jorns, Patton, Schmidt, Yen, Yu and Ye

This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

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DUV WSI with corresponding H&E image and regional importance map for malignant and normal/benign samples: Regional importance areas contain high entropy, high content/semantics (3rd column). It focuses on the strong semantic areas to help in the classification. Note that the images are inputs into a pre-trained DenseNet169 with ImageNet weights, and Grad-CAM++ extracted the heatmaps where the relevancy of the tissue areas is determined from 0 to 1. Red and green bounding boxes in DUV WSIs (last column) represent malignant and normal/benign patches thresholded by regional importance, respectively. Large DUV WSIs are scaled down for visualization.