A morphology-guided ViT-CNN approach for the detection of lung and colon cancer using H&E-stained biopsy images.

Authors

Pranjal Das, Rajagopal Kumar, Dushmanta Kumar Das

Published in

Revista espanola de patologia : publicacion oficial de la Sociedad Espanola de Anatomia Patologica y de la Sociedad Espanola de Citologia. Volume 59. Issue 3. Pages 100885. Jul 06, 2026. Epub Jul 06, 2026.

Abstract

The accurate categorisation of histopathological images is crucial for the reliable identification of morphologically similar cancers, including lung and colon cancer. According to the WHO and IARC, lung cancer accounted for approximately 2.48 million new cases and 1.8 million deaths worldwide in 2022, making it the leading cause of cancer-related mortality worldwide. Colorectal cancer also represents a major global health burden, with more than 1.9 million new cases and approximately 930,000 deaths reported in 2020 alone. These rising burdens highlight the need for robust computational pathology systems capable of accurate morphological discrimination. In this paper, we propose MorphoViT-CNN, a hybrid model that combines a Vision Transformer for capturing global contextual information with Convolutional Neural Networks (CNNs) for extracting fine grained local features. A morphology-aware segmentation module is designed to incorporate cellular shape, size, and texture attributes, while self-distillation improves the consistency of internal representations without requiring external supervision. Supervised contrastive learning further enhances inter-class separability, particularly in label-limited settings. The model was evaluated on the LC25000 dataset using five-fold cross validation, and ablation studies were conducted to assess the contribution of each component. MorphoViT-CNN demonstrated superior accuracy and precision-recall performance compared with leading CNN and transformer-based baselines, while significantly reducing misclassification among challenging histological subtypes. Its interpretable and multi-scale architecture provides a scalable framework for integration into computational pathology workflows. Future work will explore multimodal integration and foundation model pre-training to further enhance its applicability to precision oncology.

PMID:
42407139
Bibliographic data and abstract were imported from PubMed on 07 Jul 2026.

Read full publication at:
Please sign in to see all details.

Stats

1-terrible, 9-excellent. How would you rate this publication? Sign in in to submit your rating.

Post a comment

You need to be signed in to post comments. You can sign in here.

Comments

There are no comments yet.