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arXiv:2606. 25009v2 Announce Type: replace-cross Abstract: Ultrasound is a non-invasive, real-time, and cost-effective imaging technique widely used in clinical diagnosis.
arXiv:2605. 12567v2 Announce Type: replace-cross Abstract: The inherent electronic and speckle noise complicates clinical interpretation of ultrasound images.
arXiv:2605.12567v3 Announce Type: replace-cross Abstract: The inherent electronic noise, side lobes, and speckle noise complicate clinical interpretation of ultrasound images. Conventional denoising...
Risk‑Routed Implicit Boundary Refinement (RIBR) is a compact framework designed to improve boundary delineation in medical ultrasound images. It refines convolutional segmentation by applying spatially weighted implicit residuals, where a coordinate‑conditioned implicit branch predicts logit corrections and a learned gate modulates their influence based on predictive uncertainty and local appearance. Geometry‑ and speckle‑aware regularization provides contour supervision, and RIBR outperforms ten baselines across nine datasets, achieving a macro‑average Dice of 76.67% and the lowest Hausdorff distance on all datasets.
The paper introduces Consecutive Posterior Fusion Denoising Diffusion Null‑Space Models (CPF‑DDNM), an inference‑time method that fuses successive measurement‑aware estimates to better recover unobservable image structures in severely ill‑posed imaging inverse problems. CPF‑DDNM builds on DDNM’s range/null‑space decomposition, preserving measurement‑determined components while refining prior‑driven null‑space estimates. Experiments on sparse‑view CT, low‑dose CT, and medical image super‑resolution demonstrate consistent improvements over DDNM and competitive performance against other diffusion‑based inverse solvers.
Sparse-view computed tomography (CT) reduces radiation dose and acquisition time but remains severely ill-posed because incomplete projections poorly constrain null-space information. Existing learnin...