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arXiv · 2507.10250

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

Ashkan Shakarami·Lorenzo Nicole·Rocco Cappellesso·Angelo Paolo Dei Tos·Stefano Ghidoni
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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.

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Ashkan Shakarami, Lorenzo Nicole, Rocco Cappellesso, Angelo Paolo Dei Tos, Stefano Ghidoni. 2025-07-14. DepViT-CAD: Deployable Vision Transformer-Based Cancer Diagnosis in Histopathology. https://arxiv.org/abs/2507.10250

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