DepViT-CAD: Deployable Vision Transformer-Based Cancer Diagnosis in Histopathology

TL;DR

DepViT-CAD is a deployable AI system utilizing a novel Multi-Attention Vision Transformer for accurate, scalable, and validated multi-class cancer diagnosis from histopathology slides, enhancing clinical decision-making.

Contribution

Introduction of MAViT, a new Multi-Attention Vision Transformer, and its deployment in DepViT-CAD for robust, real-world cancer diagnosis from histopathological images.

Findings

Achieved over 94% sensitivity in independent cohorts

Validated on large-scale real-world datasets

Demonstrated robustness across diverse tumor types

Abstract

Accurate and timely cancer diagnosis from histopathological slides is vital for effective clinical decision-making. This paper introduces DepViT-CAD, a deployable AI system for multi-class cancer diagnosis in histopathology. At its core is MAViT, a novel Multi-Attention Vision Transformer designed to capture fine-grained morphological patterns across diverse tumor types. MAViT was trained on expert-annotated patches from 1008 whole-slide images, covering 11 diagnostic categories, including 10 major cancers and non-tumor tissue. DepViT-CAD was validated on two independent cohorts: 275 WSIs from The Cancer Genome Atlas and 50 routine clinical cases from pathology labs, achieving diagnostic sensitivities of 94.11% and 92%, respectively. By combining state-of-the-art transformer architecture with large-scale real-world validation, DepViT-CAD offers a robust and scalable approach for AI-assisted cancer diagnostics. To support transparency and reproducibility, software and code will be made publicly available at GitHub.