{"ID":2857107,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2510.10196","arxiv_id":"2510.10196","title":"From Generic to Specialized: A Subspecialty Diagnostic System Powered by Self-Supervised Learning for Cervical Histopathology","abstract":"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\u0026A, 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.","short_abstract":"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 subs...","url_abs":"https://arxiv.org/abs/2510.10196","url_pdf":"https://arxiv.org/pdf/2510.10196v1","authors":"[\"Yizhi Wang\",\"Li Chen\",\"Qiang Huang\",\"Tian Guan\",\"Xi Deng\",\"Zhiyuan Shen\",\"Jiawen Li\",\"Xinrui Chen\",\"Bin Hu\",\"Xitong Ling\",\"Taojie Zhu\",\"Zirui Huang\",\"Deshui Yu\",\"Yan Liu\",\"Jiurun Chen\",\"Lianghui Zhu\",\"Qiming He\",\"Yiqing Liu\",\"Diwei Shi\",\"Hanzhong Liu\",\"Junbo Hu\",\"Hongyi Gao\",\"Zhen Song\",\"Xilong Zhao\",\"Chao He\",\"Ming Zhao\",\"Yonghong He\"]","published":"2025-10-11T12:22:35Z","proceeding":"cs.CV","tasks":"[\"cs.CV\"]","methods":"[]","has_code":false}