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| Main Authors: | , , , , , , , , , , , , , , , , , , , , , , , , , , |
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| Format: | Preprint |
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2025
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| Online Access: | https://arxiv.org/abs/2505.21928 |
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| _version_ | 1866908395847548928 |
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| author | Zhu, Lianghui Ling, Xitong Ouyang, Minxi Liu, Xiaoping Guan, Tian Fu, Mingxi Cheng, Zhiqiang Fu, Fanglei Zeng, Maomao Liu, Liming Duan, Song Huang, Qiang Xiao, Ying Li, Jianming Lu, Shanming Piao, Zhenghua Zhu, Mingxi Jin, Yibo Xu, Shan He, Qiming Wang, Yizhi Cheng, Junru Wang, Xuanyu Xie, Luxi Li, Houqiang Tian, Sufang He, Yonghong |
| author_facet | Zhu, Lianghui Ling, Xitong Ouyang, Minxi Liu, Xiaoping Guan, Tian Fu, Mingxi Cheng, Zhiqiang Fu, Fanglei Zeng, Maomao Liu, Liming Duan, Song Huang, Qiang Xiao, Ying Li, Jianming Lu, Shanming Piao, Zhenghua Zhu, Mingxi Jin, Yibo Xu, Shan He, Qiming Wang, Yizhi Cheng, Junru Wang, Xuanyu Xie, Luxi Li, Houqiang Tian, Sufang He, Yonghong |
| contents | Gastrointestinal (GI) diseases represent a clinically significant burden, necessitating precise diagnostic approaches to optimize patient outcomes. Conventional histopathological diagnosis suffers from limited reproducibility and diagnostic variability. To overcome these limitations, we develop Digepath, a specialized foundation model for GI pathology. Our framework introduces a dual-phase iterative optimization strategy combining pretraining with fine-screening, specifically designed to address the detection of sparsely distributed lesion areas in whole-slide images. Digepath is pretrained on over 353 million multi-scale images from 210,043 H&E-stained slides of GI diseases. It attains state-of-the-art performance on 33 out of 34 tasks related to GI pathology, including pathological diagnosis, protein expression status prediction, gene mutation prediction, and prognosis evaluation. We further translate the intelligent screening module for early GI cancer and achieve near-perfect 99.70% sensitivity across nine independent medical institutions. This work not only advances AI-driven precision pathology for GI diseases but also bridge critical gaps in histopathological practice. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2505_21928 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Subspecialty-Specific Foundation Model for Intelligent Gastrointestinal Pathology Zhu, Lianghui Ling, Xitong Ouyang, Minxi Liu, Xiaoping Guan, Tian Fu, Mingxi Cheng, Zhiqiang Fu, Fanglei Zeng, Maomao Liu, Liming Duan, Song Huang, Qiang Xiao, Ying Li, Jianming Lu, Shanming Piao, Zhenghua Zhu, Mingxi Jin, Yibo Xu, Shan He, Qiming Wang, Yizhi Cheng, Junru Wang, Xuanyu Xie, Luxi Li, Houqiang Tian, Sufang He, Yonghong Image and Video Processing Artificial Intelligence Computer Vision and Pattern Recognition Machine Learning Gastrointestinal (GI) diseases represent a clinically significant burden, necessitating precise diagnostic approaches to optimize patient outcomes. Conventional histopathological diagnosis suffers from limited reproducibility and diagnostic variability. To overcome these limitations, we develop Digepath, a specialized foundation model for GI pathology. Our framework introduces a dual-phase iterative optimization strategy combining pretraining with fine-screening, specifically designed to address the detection of sparsely distributed lesion areas in whole-slide images. Digepath is pretrained on over 353 million multi-scale images from 210,043 H&E-stained slides of GI diseases. It attains state-of-the-art performance on 33 out of 34 tasks related to GI pathology, including pathological diagnosis, protein expression status prediction, gene mutation prediction, and prognosis evaluation. We further translate the intelligent screening module for early GI cancer and achieve near-perfect 99.70% sensitivity across nine independent medical institutions. This work not only advances AI-driven precision pathology for GI diseases but also bridge critical gaps in histopathological practice. |
| title | Subspecialty-Specific Foundation Model for Intelligent Gastrointestinal Pathology |
| topic | Image and Video Processing Artificial Intelligence Computer Vision and Pattern Recognition Machine Learning |
| url | https://arxiv.org/abs/2505.21928 |