# Interpretable self-supervised contrastive learning for colorectal cancer histopathology: GRADCAM visualization

## TL;DR

This paper introduces a new interpretable AI method for colorectal cancer diagnosis using self-supervised learning and visual explanations.

## Contribution

A novel interpretable framework combining self-supervised contrastive learning with Grad-CAM for histopathology classification.

## Key findings

- The model achieves 85.86% classification accuracy for hyperplastic polyps and sessile serrated adenomas.
- Grad-CAM visualizations highlight critical regions in histopathological images for model decisions.
- The approach outperforms conventional CNN methods in diagnostic accuracy and interpretability.

## 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.