arXiv Computer Vision
PhaseAT: Fourier Phase Adversarial Training for Medical Image Domain Generalization
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Leveraging Phase Information to Boost Unrolled Network Learning for Image Deblurring
arXiv:2607. 00251v1 Announce Type: cross Abstract: While most image deblurring techniques directly restore the spatial image variable, we propose an amplitude and phase decomposition recognizing the importance of accurate phase estimation in recovering sharp image details.
arXiv AI
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The Importance of Phase in Neural Representations: An Internal Oppenheim-Lim Test of Image Classifiers
arXiv:2606. 17037v1 Announce Type: cross Abstract: Oppenheim and Lim (1981) showed that natural images stay recognizable when reconstructed from their Fourier phase alone, while the magnitude carries little of their identity.
arXiv Computer Vision
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SymmAdapt: Symmetrical Flow Matching for Source-Free Domain Adaptation in Medical Image Segmentation
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arXiv Computer Vision
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Compositional Reward Models for Conditional Medical Image Generation
The paper introduces PRISM, a Compositional Reward Model framework that decomposes image quality into multiple verifier‑grounded stages for conditional medical image generation. By assigning distinct rewards for fine‑to‑coarse properties—such as intensity, texture, structural alignment, and semantic fidelity—and combining them via a Hierarchical Constrained Propagation mechanism, PRISM addresses shortcomings of single‑scalar reward approaches. Experiments on PanNuke, CeDeM, and ISIC datasets show that data generated with PRISM improves downstream model performance, achieving higher mDice, lower MRE, and increased F1 scores compared to baseline methods.
arXiv Machine Learning
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Simple, Safe, and Overlooked: Reclaiming Sustainable Domain Generalization with Statistical Color Matching
The paper introduces Colorist, a data‑augmentation method that uses classical statistical color matching to generate domain‑shifted medical images. By applying global mean‑standard‑deviation matching in RGB space, Colorist creates structurally intact variations without neural networks, outperforming deep generative models in fidelity and color alignment. Across multiple medical imaging datasets, it boosts balanced accuracy by up to 9% over state‑of‑the‑art domain‑generalization regularizers and 13% over no augmentation, while reducing computational cost and preserving anatomical structure.
arXiv AI
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ActiveAugment: Online Active Learning for Augmentation Selection in Deep Learning
arXiv:2608.28923v1 Announce Type: cross Abstract: Data augmentation is a cornerstone of deep learning pipelines, yet existing strategies treat it as a static, model-agnostic preprocessing step, eithe...