Article

Interpretable Self-Supervised Contrastive Learning for Colorectal Cancer Histopathology: GradCAM Visualization

2025-04-23

Abstract excerpt

<title>Abstract</title> <p>Accurate analysis of colorectal cancer from histopathological images is pivotal for reliable diagnosis and treatment planning. In this study, I introduce a novel framework that utilizes self-supervised contrastive learning (SSCL) for feature extraction and downstream classification of two classes of colorectal cancer - hyperplastic polyp (HP) and sessile serrated adenoma (SSA). By pre-t...

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Literature Corpus work: 369c7bc2-fff3-5a98-9957-63645c4b0b4f
DOI: 10.21203/rs.3.rs-6414312/v1

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