mlsanityData to 14 Oct 2026

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

arXiv 2507.10250 ↗First posted 14 Jul 2025eess.IV cs.AI cs.CVcited by 1

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Abstract (arXiv)

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.

Key facts. Cited by 1 paper in the map (data through 14 Oct 2026). Builds on 21 of its 49 references that are in the map. First posted on arXiv 14 Jul 2025.

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21 of 49 references.

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9 more
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