ArticleBioinformation2025
Interpretable self-supervised contrastive learning for colorectal cancer histopathology: GRADCAM visualization.
Article in Bioinformation, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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2 authors.
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Abstract
Accurate colorectal cancer diagnosis from histopathological images is crucial for effective treatment. Therefore, it is of interest to describe a novel framework that combines self-supervised contrastive learning (SSCL) with Grad-CAM-based interpretability for classifying hyperplastic polyp (HP) and sessile serrated adenoma (SSA). A ResNet50 encoder is first pre-trained using SSCL to learn rich feature representations from unlabeled images, minimizing the need for manual annotations which are then fine-tuned in a supervised setting, achieving a classification accuracy of 85.86%. Grad-CAM is used to generate visual explanations, highlighting critical regions influencing the model's decisions. This interpretable, data-efficient approach outperforms conventional CNN methods, offering improved diagnostic accuracy and enhanced trust in automated pathology.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.