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PrPSeg: Universal Proposition Learning for Panoramic Renal Pathology Segmentation
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Proposes PrPSeg, a token-based dynamic-head segmentation network with a universal proposition matrix (Aristotle-style relations) and anatomy loss for panoramic 8-class renal pathology segmentation from regions to cells.
Problem
Segmenting multi-level renal pathology structures (regions, functional units, cells) with spatial/subset-superset relationships from partially labeled data, while allowing class extension without changing the architecture.
Approach
Encodes kidney anatomy as a universal proposition matrix (affirmative/negative relations from Aristotelian logic), uses class- and scale-aware tokens feeding a controller that generates a tiny dynamic binary segmentation head, and adds an anatomy loss that enforces subset/superset/exclusion constraints between labeled and unlabeled class predictions.
Key results
- PrPSeg achieves 78.66% average Dice over 8 classes, versus 76.66% for Omni-Seg and 62.38% for Swin-UNETR.
- Adding token-based dynamic head and universal proposition learning to Omni-Seg raises average Dice from 76.66% to 77.89% (TDH+UPL on Omni-Seg is 77.89%).
- Swin-UNETR improves from 62.38% to 70.97% average Dice with the token-based dynamic head alone.
Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Use PrPSeg instead of Omni-Seg | Panoramic renal pathology segmentation · Renal pathology test set — Average | Dice (%): 76.66 -> 78.66 | Helped |
| Use PrPSeg instead of Omni-Seg | Panoramic renal pathology segmentation · Renal pathology test set — class-wise Dice | Dice (%) | Mixed |
| Add token-based dynamic head (TDH) (ablation) | Swin-Unetr · Renal pathology — Average | Dice (%): 62.38 -> 70.97 | Helped |
| Add token-based dynamic head (TDH) (ablation) | Omni-Seg · Renal pathology — Average | Dice (%): 76.66 -> 77.89 | Helped |
| Add token-based dynamic head (TDH) (ablation) | Swin-Unetr · Renal pathology — Cells | Dice (%): 57.19 -> 49.90 | Hurt |
| Add token-based dynamic head (TDH) (ablation) | Omni-Seg · Renal pathology — Functional units | Dice (%): 83.98 -> 86.39 | Helped |
| Add universal proposition learning (UPL) (ablation) | Omni-Seg + TDH · Renal pathology — Average | Dice (%): 77.89 -> 78.66 | Helped |
Surprising findings
- Multi-head and standard Transformer baselines such as Unetr and Swin-Unetr score lower on average than the simpler Omni-Seg dynamic-head CNN.
Limitations
- Evaluated on an in-house dataset with a limited number of cell-level WSIs (11)
- Anatomy loss and proposition matrix rely on manually designed anatomical relations
Experimental setup
Datasets: In-house renal region WSIs (PAS) NEPTUNE study WSIs In-house renal cell PAS WSIs
Benchmarks: 8-class renal pathology panoramic segmentation (in-house dataset)
Metrics: Dice similarity coefficient
Baselines: U-Nets DeepLabV3 Residual-U-Net Multi-kidney Omni-Seg Segmenter SegFormer Unetr Swin-Unetr
Builds on: Omni-Seg Vision Transformer tokens CycleGAN image pooling
Closest papers by meaning
- One-to-Multiple: A Progressive Style Transfer Unsupervised Domain-Adaptive Framework for Kidney Tumor Segmentation (NeurIPS 2024 · similarity 0.87)
- Weakly Supervised Gland Segmentation with Class Semantic Consistency and Purified Labels Filtration (AAAI 2025 · similarity 0.82)
- Pancakes: Consistent Multi-Protocol Image Segmentation Across Biomedical Domains (NeurIPS 2025 · similarity 0.82)
- Combinatorial CNN-Transformer Learning with Manifold Constraints for Semi-supervised Medical Image Segmentation (AAAI 2024 · similarity 0.81)
- Show and Segment: Universal Medical Image Segmentation via In-Context Learning (CVPR 2025 · similarity 0.81)
- ZePT: Zero-Shot Pan-Tumor Segmentation via Query-Disentangling and Self-Prompting (CVPR 2024 · similarity 0.81)
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