用自监督学习打造宫颈病理细分诊断系统，准确率超现有模型。
From Generic to Specialized: A Subspecialty Diagnostic System Powered by Self-Supervised Learning for Cervical Histopathology
- 两阶段预训练：1.9亿组织切片+250万图文对，构建宫颈特异性特征提取器
- 跨五中心3173例前瞻性测试，筛查敏感度达99.38%，泛化能力强
- 支持罕见癌分类与多模态问答，适合临床辅助诊断场景
宫颈癌仍是重大恶性肿瘤，需大量复杂组织病理学评估和全面支持工具。尽管深度学习有潜力，但现有模型仍存在准确率与泛化能力不足的问题。通用基础模型虽覆盖范围广，却难以捕捉亚专科特异性特征与任务适应性。本文提出宫颈亚专科病理（CerS-Path）诊断系统，通过两个协同预训练阶段实现：在约190万张组织切片（来自14万张幻灯片）上进行自监督学习，构建宫颈特异性特征提取器；再利用250万张图像-文本对进行多模态增强，并集成多种下游诊断功能。该系统支持八项诊断功能，包括罕见癌症分类与多模态问答，其应用范围和临床实用性超越先前基础模型。全面评估显示显著提升，跨五个中心的前瞻性测试中3173例病例保持99.38%的筛查敏感度，且具备优异泛化能力，凸显其在亚专科诊断转化与宫颈癌筛查中的潜力。
原文摘要 · Abstract (English)
Cervical cancer remains a major malignancy, necessitating extensive and complex histopathological assessments and comprehensive support tools. Although deep learning shows promise, these models still lack accuracy and generalizability. General foundation models offer a broader reach but remain limited in capturing subspecialty-specific features and task adaptability. We introduce the Cervical Subspecialty Pathology (CerS-Path) diagnostic system, developed through two synergistic pretraining stages: self-supervised learning on approximately 190 million tissue patches from 140,000 slides to build a cervical-specific feature extractor, and multimodal enhancement with 2.5 million image-text pairs, followed by integration with multiple downstream diagnostic functions. Supporting eight diagnostic functions, including rare cancer classification and multimodal Q&A, CerS-Path surpasses prior foundation models in scope and clinical applicability. Comprehensive evaluations demonstrate a significant advance in cervical pathology, with prospective testing on 3,173 cases across five centers maintaining 99.38% screening sensitivity and excellent generalizability, highlighting its potential for subspecialty diagnostic translation and cervical cancer screening.
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