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.
@article{arxiv.2510.10196,
title = {From Generic to Specialized: A Subspecialty Diagnostic System Powered by Self-Supervised Learning for Cervical Histopathology},
author = {Yizhi Wang and Li Chen and Qiang Huang and Tian Guan and Xi Deng and Zhiyuan Shen and Jiawen Li and Xinrui Chen and Bin Hu and Xitong Ling and Taojie Zhu and Zirui Huang and Deshui Yu and Yan Liu and Jiurun Chen and Lianghui Zhu and Qiming He and Yiqing Liu and Diwei Shi and Hanzhong Liu and Junbo Hu and Hongyi Gao and Zhen Song and Xilong Zhao and Chao He and Ming Zhao and Yonghong He},
journal= {arXiv preprint arXiv:2510.10196},
year = {2025}
}