Article

Deep Learning Features Encode Interpretable Morphologies within Histological Images

2022-02-14

Abstract excerpt

Convolutional neural networks (CNNs) are revolutionizing digital pathology by enabling machine learning-based classification of a variety of phenotypes from hematoxylin and eosin (H&E) whole slide images (WSIs), but the interpretation of CNNs remains difficult. Most studies have considered interpretability in a post hoc fashion, e.g. by presenting example regions with strongly predicted class labels. However, such...

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Literature Corpus work: e4ceae51-a024-5536-bdc4-f64aaa319547
DOI: 10.21203/rs.3.rs-865341/v2

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