LAW & ORDER: Adaptive Spatial Weighting for Medical Diffusion and Segmentation
#adaptive spatial weighting #medical diffusion #segmentation #medical imaging #AI #diffusion models #image analysis
📌 Key Takeaways
- LAW & ORDER introduces an adaptive spatial weighting method for medical imaging tasks.
- The technique enhances diffusion models and segmentation processes in medical applications.
- It aims to improve accuracy by dynamically adjusting spatial importance in image analysis.
- The approach is designed to handle complex medical data more effectively than standard methods.
📖 Full Retelling
arXiv:2603.04795v1 Announce Type: cross
Abstract: Medical image analysis relies on accurate segmentation, and benefits from controllable synthesis (of new training images). Yet both tasks of the cyclical pipeline face spatial imbalance: lesions occupy small regions against vast backgrounds. In particular, diffusion models have been shown to drift from prescribed lesion layouts, while efficient segmenters struggle on spatially uncertain regions. Adaptive spatial weighting addresses this by learn
🏷️ Themes
Medical Imaging, AI Algorithms
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--> Computer Science > Computer Vision and Pattern Recognition arXiv:2603.04795 [Submitted on 5 Mar 2026] Title: LAW & ORDER: Adaptive Spatial Weighting for Medical Diffusion and Segmentation Authors: Anugunj Naman , Ayushman Singh , Gaibo Zhang , Yaguang Zhang View a PDF of the paper titled LAW & ORDER: Adaptive Spatial Weighting for Medical Diffusion and Segmentation, by Anugunj Naman and 3 other authors View PDF HTML Abstract: Medical image analysis relies on accurate segmentation, and benefits from controllable synthesis (of new training images). Yet both tasks of the cyclical pipeline face spatial imbalance: lesions occupy small regions against vast backgrounds. In particular, diffusion models have been shown to drift from prescribed lesion layouts, while efficient segmenters struggle on spatially uncertain regions. Adaptive spatial weighting addresses this by learning where to allocate computational resources. This paper introduces a pair of network adapters: 1) Learnable Adaptive Weighter which predicts per-pixel loss modulation from features and masks for diffusion training, stabilized via a mix of normalization, clamping, and regularization to prevent degenerate solutions; and 2) Optimal Region Detection with Efficient Resolution which applies selective bidirectional skip attention at late decoder stages for efficient segmentation. Experiments on polyp and kidney tumor datasets demonstrate that LAW achieves 20% FID generative improvement over a uniform baseline (52.28 vs. 65.60), with synthetic data then improving downstream segmentation by 4.9% Dice coefficient (83.2% vs. 78.3%). ORDER reaches 6.0% Dice improvement on MK-UNet (81.3% vs. 75.3%) with 0.56 GFLOPs and just 42K parameters, remaining 730x smaller than the standard nnUNet. Subjects: Computer Vision and Pattern Recognition (cs.CV) ; Artificial Intelligence (cs.AI) Cite as: arXiv:2603.04795 [cs.CV] (or arXiv:2603.04795v1 [cs.CV] for this version) https://doi.org/10.48550/arXiv.2603.04795 Focus to l...
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