arXiv:2608.29820v1 Announce Type: new Abstract: Ultrasound B-mode imaging commonly suffers from speckle noise and artifacts, requiring a delicate balance between contrast, resolution, and preservatio...
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.
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.
arXiv:2607. 25275v1 Announce Type: cross Abstract: Real-world Image Restoration (Real-IR) aims to recover high-quality (HQ) images from complex and unknown degradations.
arXiv:2605. 12567v2 Announce Type: replace-cross Abstract: The inherent electronic and speckle noise complicates clinical interpretation of ultrasound images.
Match4Annotate is a test‑time framework that transfers user‑specified annotations from a labeled ultrasound video to an unlabeled target video without requiring manual initialization. It uses a spatiotemporal implicit feature representation, a continuous implicit feature flow for alignment, and flow‑guided annotation transfer to unify sparse point and dense mask transfer. The method achieves state‑of‑the‑art performance on four clinical ultrasound datasets, outperforming dense feature‑matching baselines and one‑shot segmentation methods, and works without task‑specific training on a single consumer GPU.
US-JEPA introduces a self‑supervised framework for ultrasound imaging that predicts masked latent representations instead of raw pixels, using a frozen, domain‑specific teacher to provide stable targets. This approach avoids the hyperparameter sensitivity and computational cost of traditional online teachers, enabling the student model to build upon the teacher’s semantic priors. The authors benchmark US‑JEPA against all publicly available ultrasound foundation models on UltraBench, showing competitive or superior performance across multiple organs and pathological conditions under linear probing.
arXiv:2607. 19137v1 Announce Type: cross Abstract: Inferring contrast enhancement from one pre-contrast breast MRI slice is underdetermined: post-contrast appearance contains physiological information that is not uniquely encoded in baseline anatomy.
UltraPIPS introduces domain‑specific foundation models for measuring perceptual similarity in B‑mode ultrasound images. The study shows that ultrasound‑trained LPIPS backbones better correlate with downstream tasks such as classification, segmentation, and reconstruction than natural‑image or general medical models. Optimizing LPIPS loss with an ultrasound backbone yields a strong balance between reconstruction quality and realism, and the authors provide an open‑source library for these metrics.
arXiv:2609.30988v1 Announce Type: new Abstract: Real-world image super-resolution (SR) requires recovering perceptually realistic high-resolution images from complex low-resolution observations while...
The paper introduces CM-RED, a fast MRI reconstruction method that combines a pretrained consistency model with the regularization by denoising framework. By integrating controlled noise injection into accelerated proximal gradient updates, CM-RED achieves high‑quality reconstructions on fastMRI knee and brain datasets with only four network function evaluations. It consistently outperforms existing diffusion‑ and consistency‑based approaches in quantitative metrics, visual fidelity, and robustness to hyperparameter changes.
The paper introduces a method for zero‑shot video restoration that improves upon prior approaches by using DDIM inversion on corrupted video frames to generate noise that preserves temporal correlations. An adaptive noise blending strategy is added to reduce degradation patterns while gradually moving toward Gaussian noise, and a measurement‑guided trajectory attention mechanism further enhances temporal consistency. Experiments on DAVIS, REDS4, and Set8 datasets show that this approach outperforms existing zero‑shot video restoration methods.