# PrPSeg: Universal Proposition Learning for Panoramic Renal Pathology Segmentation

arXiv: https://arxiv.org/abs/2402.19286 · HTML: https://mlsanity.com/paper/2402.19286/

## Key facts

- Cited by 14 papers in the map (data through 14 Oct 2026).
- Builds on 17 of its 54 references that are in the map.
- First posted on arXiv 29 Feb 2024.

## Builds on (most cited first, 17 of 17)

- [An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale](https://mlsanity.com/paper/2010.11929/) (2020)
- [SegFormer: Simple and Efficient Design for Semantic Segmentation with Transformers](https://mlsanity.com/paper/2105.15203/) (2021)
- [UNETR: Transformers for 3D Medical Image Segmentation](https://mlsanity.com/paper/2103.10504/) (2021)
- [Segmenter: Transformer for Semantic Segmentation](https://mlsanity.com/paper/2105.05633/) (2021)
- [Swin UNETR: Swin Transformers for Semantic Segmentation of Brain Tumors in MRI Images](https://mlsanity.com/paper/2201.01266/) (2022)
- [Scaling Vision Transformers to Gigapixel Images via Hierarchical Self-Supervised Learning](https://mlsanity.com/paper/2206.02647/) (2022)
- [Med3D: Transfer Learning for 3D Medical Image Analysis](https://mlsanity.com/paper/1904.00625/) (2019)
- [DoDNet: Learning to segment multi-organ and tumors from multiple partially labeled datasets](https://mlsanity.com/paper/2011.10217/) (2020)
- [Deep Hierarchical Semantic Segmentation](https://mlsanity.com/paper/2203.14335/) (2022)
- [Multi-organ Segmentation over Partially Labeled Datasets with Multi-scale Feature Abstraction](https://mlsanity.com/paper/2001.00208/) (2020)
- [Unsupervised Hierarchical Semantic Segmentation with Multiview Cosegmentation and Clustering Transformers](https://mlsanity.com/paper/2204.11432/) (2022)
- [Instance-based Vision Transformer for Subtyping of Papillary Renal Cell Carcinoma in Histopathological Image](https://mlsanity.com/paper/2106.12265/) (2021)
- [Iterative annotation to ease neural network training: Specialized machine learning in medical image analysis](https://mlsanity.com/paper/1812.07509/) (2018)
- [Omni-Seg: A Scale-aware Dynamic Network for Renal Pathological Image Segmentation](https://mlsanity.com/paper/2206.13632/) (2022)
- [CNN Cascades for Segmenting Whole Slide Images of the Kidney](https://mlsanity.com/paper/1708.00251/) (2017)
- [Evaluating Transformer-based Semantic Segmentation Networks for Pathological Image Segmentation](https://mlsanity.com/paper/2108.11993/) (2021)
- [Democratizing Pathological Image Segmentation with Lay Annotators via Molecular-empowered Learning](https://mlsanity.com/paper/2306.00047/) (2023)

## Built on by (most cited first, 14 of 14)

- [HATs: Hierarchical Adaptive Taxonomy Segmentation for Panoramic Pathology Image Analysis](https://mlsanity.com/paper/2407.00596/) (2024)
- [HoloHisto: End-to-end Gigapixel WSI Segmentation with 4K Resolution Sequential Tokenization](https://mlsanity.com/paper/2407.03307/) (2024)
- [Streamline pathology foundation model by cross-magnification distillation](https://mlsanity.com/paper/2509.23097/) (2025)
- [PFPs: Prompt-guided Flexible Pathological Segmentation for Diverse Potential Outcomes Using Large Vision and Language Models](https://mlsanity.com/paper/2407.09979/) (2024)
- [RankByGene: Gene-Guided Histopathology Representation Learning Through Cross-Modal Ranking Consistency](https://mlsanity.com/paper/2411.15076/) (2024)
- [IRS: Incremental Relationship-guided Segmentation for Digital Pathology](https://mlsanity.com/paper/2505.22855/) (2025)
- [BackSplit: The Importance of Sub-dividing the Background in Biomedical Lesion Segmentation](https://mlsanity.com/paper/2511.19394/) (2025)
- [ZeroReg3D: A Zero-shot Registration Pipeline for 3D Consecutive Histopathology Image Reconstruction](https://mlsanity.com/paper/2506.21923/) (2025)
- [DyMorph-B2I: Dynamic and Morphology-Guided Binary-to-Instance Segmentation for Renal Pathology](https://mlsanity.com/paper/2508.15208/) (2025)
- [M^3-GloDets: Multi-Region and Multi-Scale Analysis of Fine-Grained Diseased Glomerular Detection](https://mlsanity.com/paper/2508.17666/) (2025)
- [An Augmentation-based Model Re-adaptation Framework for Robust Image Segmentation](https://mlsanity.com/paper/2409.09530/) (2024)
- [CASC-AI: Consensus-aware Self-corrective Learning for Noise Cell Segmentation](https://mlsanity.com/paper/2502.07302/) (2025)
- [MORI-Seg: Learning Morphological Geometry for Instance Segmentation without Instance Annotations](https://mlsanity.com/paper/2605.28261/) (2026)
- [Seeing Abnormal from Normal: Glomerular Abnormality in Representations of Normal Renal Morphology](https://mlsanity.com/paper/2609.19444/) (2026)

## Abstract (arXiv)

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.