Title
Interpretable self-supervised contrastive learning for colorectal cancer histopathology: GRADCAM visualization
Authors
Tarun Jain & Andrew M. Lynn*
Affiliation
School of Computational and Integrative Sciences (SCIS), Jawaharlal Nehru University, New Delhi, India; *Corresponding author
Tarun Jain - E-mail: tarun32_sit@jnu.ac.in
Andrew M. Lynn - E-mail: andrew@jnu.ac.in
Article Type
Research Article
Date
Received July 1, 2025; Revised July 31, 2025; Accepted July 31, 2025, Published July 31, 2025
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.
Keywords
Self supervised contrastive learning, colorectal cancer histopathology, deep learning, interpretable AI, GradCAM
Citation
Jain & Lynn, Bioinformation 21(7): 1836-1842 (2025)
Edited by
P Kangueane
ISSN
0973-2063
Publisher
License
This is an Open Access article which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly credited. This is distributed under the terms of the Creative Commons Attribution License.
