Hugging Face Trending Papers

Style-Driven Data Synthesis and Degradation-Aware Enhancement for Ultrasound Image Restoration

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The paper proposes a two‑stage framework to improve images from low‑cost handheld ultrasound devices by mapping them to high‑quality hospital images. First, a cycle‑consistent style‑transfer model generates pixel‑aligned low‑quality/high‑quality pairs from unaligned real scans. Second, a Dual Degradation‑Guided Low‑Rank Adaptation (DDG‑LoRA) model fine‑tunes an LQ‑to‑HQ enhancement network, achieving a 16.7% FID improvement on the USenhance2023 dataset.

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arXiv Computer Vision
Aug 27

UltraPIPS: Improving model perception in B-mode ultrasound with foundation models

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.

By Tal Grutman, Tali Ilovitsh
arXiv Computer Vision
Aug 24

Toward Vision Language Model-based Assessment of Clinical Quality and Usability of LGE-MR Images for Cardiac Ablation Planning

This study introduces a two‑stage vision‑language model framework to assess the clinical quality and usability of late gadolinium enhancement (LGE) cardiac MRI images used for atrial fibrillation ablation planning. The first stage employs a fine‑tuned VLM to generate structured radiology‑style reports on five quality criteria—Noise, Motion Artifact, LA Boundary Accuracy, PV Region Accuracy, and Under‑segmentation Severity—while the second stage uses a GPT‑based reasoning module to convert these reports into structured quality scores and a binary decision on clinical usability. Evaluated on a curated dataset of 60 image‑slice and text‑pair annotations from 20 patients, the InternVL2 model achieved the highest criterion‑level accuracy, and DeepSeek reached perfect agreement on the clinical usability decision.

By Bipasha Kundu, Abhishek Chaturvedi, Axel W. E. Wismueller, Richard Simon, Cristian A. Linte
arXiv AI
Jul 22

FedCC: A Low-Resource Federated Adaptation of Foundation Models for Robust Corpus Callosum localization in Fetal Ultrasound Images

arXiv:2607. 18283v1 Announce Type: cross Abstract: Accurate localization of the corpus callosum (CC) in fetal ultrasound (US) images is crucial for the early identification of neurodevelopmental abnormalities.

By Alessandro Di Matteo, Sara Moccia, Giuseppe Rizzo, Gianpaolo Grisolia, Ricciarda Raffaelli, Lorenzo Vasciaveo, Francesco D'Antonio, Maria Chiara Fiorentino