DepViT-CAD：基于可部署视觉Transformer的癌症病理诊断
DepViT-CAD: Deployable Vision Transformer-Based Cancer Diagnosis in Histopathology
AI总结：
本文提出可部署AI系统DepViT-CAD，其核心为多注意力视觉Transformer（MAViT），在1008张全切片图像上训练覆盖11类诊断，并在两个独立队列验证中分别取得94.11%和92%的灵敏度，为AI辅助癌症诊断提供稳健可扩展方案。
摘要：
从组织病理学切片中进行准确且及时的癌症诊断对于有效的临床决策至关重要。本文介绍了DepViT-CAD，一个用于组织病理学多类癌症诊断的可部署AI系统。其核心是MAViT，一种新颖的多注意力视觉Transformer，旨在捕捉不同肿瘤类型间的细粒度形态学模式。MAViT在来自1008张全切片图像的专家标注图像块上进行训练，涵盖11个诊断类别，包括10种主要癌症和非肿瘤组织。DepViT-CAD在两个独立队列上进行了验证：来自The Cancer Genome Atlas的275张WSI和来自病理实验室的50例常规临床病例，分别实现了94.11%和92%的诊断灵敏度。通过结合最先进的transformer架构与大规模真实世界验证，DepViT-CAD为AI辅助癌症诊断提供了一种稳健且可扩展的方法。为支持透明度和可重复性，软件和代码将在GitHub上公开。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
