GloPath: An Entity-Centric Foundation Model for Glomerular Lesion Assessment and Clinicopathological Insights

TL;DR

GloPath is presented, 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, representing a step toward clinically translatable AI in renal pathology.

Abstract

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.

Figures (21)

• Figure 1: Overview of GloPath. a, Dataset curation. The pathological images are obtained from seven cohorts including XJ-Light-1, XJ-Light-2, XJ-IF, AIDPATH-G, XJ-GIO and KPMP-G, where WSI is processed by entity detection. b, Entity-centric self-supervised pretraining based on multi-scale multi-view. c, Lesion assessment. d, Clinicopathological correlation analysis.
• Figure 2: Overview of the performance of lesion assessment. a, The best model on each lesion assessment task across lesion recognition, lesion grading, cross-modality diagnosis, and few-shot-based diagnosis. Refer to the legend in (b). b, Comparison of the distribution of the results of lesion recognition.
• Figure 3: Comparison of results of glomerular lesion analysis. a, Comparison of the distribution of F1 score of lesion recognition. b-d, Comparison of results on PAS, MT and PASM stainings. The primary vertical axis corresponds to the F1 score of each model. The secondary vertical axis corresponds to Min-Gain and Max-Gain, which are, respectively, the minimum and maximum performance gains of GloPath over all other models. Below the chart are the visualization results, where rows 1-3 are original images and the results of GloPath and UNI, respectively.
• Figure 4: Comparison of results of cross-modality diagnosis. a, Comparison of preformance on cross-modality diagnosis. b, Visualization of attention map of GloPath. Row 1-2 indicate region classification and row 3-4 indicate pattern classification. Column 1 indicates the original IF images and column 2-4 indicate the attention map from three attention heads. c-d, Validation loss curves for region and pattenr respectively.
• Figure 5: Comparison of results of few-shot based classification. a, Performance of few-shot based classification on AIDPATH-G. b, Distribution of embedding on AIDPATH-G using t-SNE. c, Visualization of Channel-wise embedding on AIDPATH-G. d, Results of few-shot for IF image classification. Row 1-2 indicate results for region, pattern respectively. Column 1-4 indicate results for different methods including LR, MLP, RF, and PTL respectively.