ArticleProceedings. IEEE Computer Society Conference on Computer Vision and Pattern Recognition2024
PrPSeg: Universal Proposition Learning for Panoramic Renal Pathology Segmentation.
Article in Proceedings. IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
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Who cites it
3 citing papers in PubMed.
- The problem of segmenting global glomerulosclerosis in gigapixel histopathological images: the borderless glomeruli.BMC nephrology · 2025Article
- Simplified Artificial Intelligence Terminology for Pathologists.Diagnostics (Basel, Switzerland) · 2025Review
- HATs: Hierarchical Adaptive Taxonomy Segmentation for Panoramic Pathology Image Analysis.Medical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention · 2024Article
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Authors and funding
14 authors.
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Abstract
Understanding the anatomy of renal pathology is crucial for advancing disease diagnostics, treatment evaluation, and clinical research. The complex kidney system comprises various components across multiple levels, including regions (cortex, medulla), functional units (glomeruli, tubules), and cells (podocytes, mesangial cells in glomerulus). Prior studies have predominantly overlooked the intricate spatial interrelations among objects from clinical knowledge. In this research, we introduce a novel universal proposition learning approach, called panoramic renal pathology segmentation (PrPSeg), designed to segment comprehensively panoramic structures within kidney by integrating extensive knowledge of kidney anatomy. In this paper, we propose (1) the design of a comprehensive universal proposition matrix for renal pathology, facilitating the incorporation of classification and spatial relationships into the segmentation process; (2) a token-based dynamic head single network architecture, with the improvement of the partial label image segmentation and capability for future data enlargement; and (3) an anatomy loss function, quantifying the inter-object relationships across the kidney.
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Registered trials
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