基于Transformer的可部署癌症病理诊断系统，准确率达94%以上。
DepViT-CAD: Deployable Vision Transformer-Based Cancer Diagnosis in Histopathology
- 采用多注意力视觉变压器捕捉肿瘤细微形态特征
- 在两个独立数据集上诊断敏感性分别达94.11%和92%
- 适合临床病理医生辅助诊断，代码开源可复现
从组织病理切片中实现精准及时的癌症诊断对临床决策至关重要。本文提出DepViT-CAD，一种可部署的基于视觉变换器的多类别癌症诊断AI系统。其核心是MAViT——一种新型多注意力视觉变换器，能捕捉多种肿瘤类型的细粒度形态模式。MAViT在1008张全切片图像的专家标注样本上训练，涵盖11个诊断类别，包括10种主要癌症和非肿瘤组织。DepViT-CAD在两个独立队列中验证：来自TCGA的275张全切片图像和来自病理科的50例常规临床病例，诊断敏感性分别为94.11%和92%。通过结合先进Transformer架构与大规模真实世界验证，DepViT-CAD为人工智能辅助癌症诊断提供了稳健且可扩展的方案。为支持透明性和可复现性，软件和代码将公开发布于GitHub。
原文摘要 · Abstract (English)
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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