GloPath: An Entity-Centric Foundation Model for Glomerular Lesion Assessment and Clinicopathological Insights
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arXiv
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| Natura: | Preprint |
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2026
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| _version_ | 1866910039281762304 |
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| author | He, Qiming Li, Jing Guan, Tian Ma, Yifei Zhao, Zimo Wang, Yanxia Chen, Hongjing Xu, Yingming Ge, Shuang Zhang, Yexing Wang, Yizhi Chen, Xinrui Zhu, Lianghui Liu, Yiqing Hou, Qingxia Zhao, Shuyan Wang, Xiaoqin Ma, Lili Hu, Peizhen Huang, Qiang Wang, Zihan Shen, Zhiyuan Cheng, Junru Zeng, Siqi Chen, Jiurun Song, Zhen He, Chao Wang, Zhe He, Yonghong |
| author_facet | He, Qiming Li, Jing Guan, Tian Ma, Yifei Zhao, Zimo Wang, Yanxia Chen, Hongjing Xu, Yingming Ge, Shuang Zhang, Yexing Wang, Yizhi Chen, Xinrui Zhu, Lianghui Liu, Yiqing Hou, Qingxia Zhao, Shuyan Wang, Xiaoqin Ma, Lili Hu, Peizhen Huang, Qiang Wang, Zihan Shen, Zhiyuan Cheng, Junru Zeng, Siqi Chen, Jiurun Song, Zhen He, Chao Wang, Zhe He, Yonghong |
| contents | Glomerular pathology is central to the diagnosis and prognosis of renal diseases, yet the heterogeneity of glomerular morphology and fine-grained lesion patterns remain challenging for current AI approaches. We present GloPath, an entity-centric foundation model trained on over one million glomeruli extracted from 14,049 renal biopsy specimens using multi-scale and multi-view self-supervised learning. GloPath addresses two major challenges in nephropathology: glomerular lesion assessment and clinicopathological insights discovery. For lesion assessment, GloPath was benchmarked across three independent cohorts on 52 tasks, including lesion recognition, grading, few-shot classification, and cross-modality diagnosis-outperforming state-of-the-art methods in 42 tasks (80.8%). In the large-scale real-world study, it achieved an ROC-AUC of 91.51% for lesion recognition, demonstrating strong robustness in routine clinical settings. For clinicopathological insights, GloPath systematically revealed statistically significant associations between glomerular morphological parameters and clinical indicators across 224 morphology-clinical variable pairs, demonstrating its capacity to connect tissue-level pathology with patient-level outcomes. Together, these results position GloPath as a scalable and interpretable platform for glomerular lesion assessment and clinicopathological discovery, representing a step toward clinically translatable AI in renal pathology. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2603_02926 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | GloPath: An Entity-Centric Foundation Model for Glomerular Lesion Assessment and Clinicopathological Insights He, Qiming Li, Jing Guan, Tian Ma, Yifei Zhao, Zimo Wang, Yanxia Chen, Hongjing Xu, Yingming Ge, Shuang Zhang, Yexing Wang, Yizhi Chen, Xinrui Zhu, Lianghui Liu, Yiqing Hou, Qingxia Zhao, Shuyan Wang, Xiaoqin Ma, Lili Hu, Peizhen Huang, Qiang Wang, Zihan Shen, Zhiyuan Cheng, Junru Zeng, Siqi Chen, Jiurun Song, Zhen He, Chao Wang, Zhe He, Yonghong Computer Vision and Pattern Recognition Glomerular pathology is central to the diagnosis and prognosis of renal diseases, yet the heterogeneity of glomerular morphology and fine-grained lesion patterns remain challenging for current AI approaches. We present GloPath, an entity-centric foundation model trained on over one million glomeruli extracted from 14,049 renal biopsy specimens using multi-scale and multi-view self-supervised learning. GloPath addresses two major challenges in nephropathology: glomerular lesion assessment and clinicopathological insights discovery. For lesion assessment, GloPath was benchmarked across three independent cohorts on 52 tasks, including lesion recognition, grading, few-shot classification, and cross-modality diagnosis-outperforming state-of-the-art methods in 42 tasks (80.8%). In the large-scale real-world study, it achieved an ROC-AUC of 91.51% for lesion recognition, demonstrating strong robustness in routine clinical settings. For clinicopathological insights, GloPath systematically revealed statistically significant associations between glomerular morphological parameters and clinical indicators across 224 morphology-clinical variable pairs, demonstrating its capacity to connect tissue-level pathology with patient-level outcomes. Together, these results position GloPath as a scalable and interpretable platform for glomerular lesion assessment and clinicopathological discovery, representing a step toward clinically translatable AI in renal pathology. |
| title | GloPath: An Entity-Centric Foundation Model for Glomerular Lesion Assessment and Clinicopathological Insights |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2603.02926 |