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arXiv AI

Ultrasound-Based Prediction of Cirrhosis Decompensation Using Large-Scale Computer Vision Models

The paper introduces an imaging-based method that uses large-scale computer vision models to analyze routine abdominal ultrasound images for predicting cirrhosis decompensation. It extracts predictive features beyond traditional laboratory risk scores, offering a non-invasive, low-cost, and scalable approach for early risk stratification. The framework combines automated ultrasound processing with modern deep learning to identify high-risk patients before clinical deterioration occurs.

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arXiv Computer Vision
Sep 11

DINO-Med: A Unified Patch-Based Adaptation Framework for Multi-Modal Medical Image Analysis Applied to Liver Fibrosis Staging

DINO-Med introduces a patch‑based framework that adapts natural‑image foundation models, specifically DINOv3, to multi‑modal medical imaging. The method uses training‑free registration, automated localization, and mask‑filtered patch extraction to aggregate patch‑level features into subject‑level diagnostics. In liver fibrosis staging, DINOv3 outperforms handcrafted radiomics, ResNet, and SAM‑Med2D features, achieving 78.4% accuracy for mild fibrosis (S1) and 75.8% for cirrhosis (S4) on the CARE 2025 cohort.

By Boya Wang, Ruizhe Li, Chao Chen, Xin Chen
multimodal
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Hugging Face Trending Papers
Sep 10

DINO-Med: A Unified Patch-Based Adaptation Framework for Multi-Modal Medical Image Analysis Applied to Liver Fibrosis Staging

The paper introduces DINO-Med, a patch‑based framework that adapts natural‑image foundation models to multi‑modal medical imaging, specifically for liver fibrosis staging. It processes raw multimodal scans through training‑free registration, automated localization, and mask‑filtered patch extraction, then aggregates patch‑level insights into subject‑level diagnostics. Using the CARE 2025 Liver Track 4 cohort, the DINOv3‑based approach achieved the highest classification accuracy (78.4% for mild fibrosis and 75.8% for cirrhosis) compared to other feature representations.

multimodal
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arXiv Machine Learning
Jul 15

Learning from Complementary Ultrasound Representations for Liver Disease Classification

arXiv:2607. 12062v1 Announce Type: cross Abstract: Differentiating non-alcoholic steatohepatitis (NASH) from non-alcoholic fatty liver disease (NAFLD) using ultrasound remains challenging due to subtle tissue alterations and the limited information available in conventional B-mode imaging.

By Sabahattin Mert Daloglu, Gokce Bekar, Ceren Coskun, Senanur Sahin, Harvey Castro, Soner Hacihaliloglu, Halley P. Letter, Ilker Hacihaliloglu
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arXiv Machine Learning
Jun 16

A Multi-Center Benchmark for Abdominal Disease Diagnosis and Report Generation from Non-Contrast CT

arXiv:2606. 16991v1 Announce Type: cross Abstract: Multiphasic contrast-enhanced CT (CECT) is widely used for abdominal lesion characterization, yet it carries inherent risks of contrast-induced nephropathy, escalates acquisition burden, and heavily contributes to radiologist workload.

By Mariam Elbakry, Aliaa Sayed Sheha, Salma Hassan Tantawy, Aya Yassin, Concetto Spampinato, Karim Lekadir, Xiaomeng Li, Marawan Elbatel
multimodalbenchmarks
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arXiv Computer Vision
Sep 7

Development and Evaluation of Ultrasound Image Learning Pipelines for MASLD Risk Stratification

The study developed deep‑learning pipelines using B‑mode and shear wave elastography (SWE) ultrasound images to stage fibrosis and identify at‑risk metabolic dysfunction‑associated steatohepatitis (MASH) in patients with MASLD. Across 250 examinations, SWE‑based learning consistently outperformed B‑mode learning, achieving higher AUROC scores for fibrosis stages F≥2, F≥3, and F4. End‑to‑end SWE models matched operator‑guided SWE performance for fibrosis staging.

By Guangyi Zhang, Xiaohong Wang, Eugene Cheah, Peng Guo, Brian A. Telfer, Theodore T. Pierce, Anthony E. Samir
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arXiv AI
Aug 17

CMCNet: Aligning Ultrasound Image Embeddings with Textual TI-RADS Representations for Fine-Grained Thyroid Classification

arXiv:2608. 13939v1 Announce Type: cross Abstract: Ultrasound is the primary imaging modality for assessing thyroid nodules, and the ACR TI-RADS framework standardizes diagnosis through five ultrasound feature categories that are aggregated into five risk levels (TR1-TR5).

By Bingxin Yu, Xueli Wang, Jerry Zhou, Wenyan Wang, Li Wen, Lan Huang, Xin Feng, Fengfeng Zhou, Kewei Li
ragmultimodalsafety
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arXiv AI
Sep 15

ProtoCAM: Interpretable Few-Shot Mask-Guided Prototypical Learning for Breast Lesion Classification in Ultrasound Imaging

ProtoCAM is an explainable few‑shot learning framework for classifying breast lesions in ultrasound images. It combines mask‑guided feature encoding, prototypical metric learning, and gradient‑based visual explanations to leverage limited annotated data. Evaluated on the BUSI dataset, ProtoCAM achieved a macro F1‑score of 0.910 in a 3‑way 5‑shot setting, outperforming standard supervised CNNs, with ResNet18 reaching 91.65% under 15‑shot conditions.

By Ashkan Ebadi
rag
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arXiv Machine Learning
Sep 25

UltraBench 2: Towards Robust Evaluation of Vision Foundation Models on Ultrasound

UltraBench 2 is a new benchmark designed to evaluate vision foundation models on ultrasound images, addressing the lack of standardized tests in this area. It covers a wide range of anatomical structures and tasks, emphasizing reproducibility and ease of use. The authors compare existing models, finding that ultrasound-specific pretraining still outperforms on classification, while general-purpose models have matched performance on segmentation.

By Ashwath Radhachandran, Adam Tupper, Christian Gagn\'e, William Speier
computer-visionbenchmarks
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