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
arXiv:2609.14010v1 Announce Type: new
Abstract: We present CirrGuide, a deep cascaded framework for cirrhotic liver segmentation and severity classification. Cirrhosis causes progressive structural c...
By Muntaqim Ahmed Raju, Ruizhe Ma
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.
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
arXiv:2607. 22173v1 Announce Type: cross Abstract: Bowel obstruction is a common and potentially life-threatening gastrointestinal condition.
By Moritz Vandenhirtz, Andrea Agostini, Dana Belde, M\'elanie Roschewitz, Ismaiel Chikh Bakri, Tilo Niemann, Andr\'e Euler, Julia E Vogt
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
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
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
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
arXiv:2606. 26561v1 Announce Type: new Abstract: Hepatitis C is a liver infection caused by a virus, which results in mild to severe inflammation of the liver.
By Abrar Alotaibi, Lujain Alnajrani, Nawal Alsheikh, Alhatoon Alanazy, Salam Alshammasi, Meshael Almusairii, Shoog Alrassan, Aisha Alansari
The paper introduces AMPLIFAI, the first public dataset of multiphase abdominal CT scans annotated with LI-RADS categories and segmented for three key LI-RADS features: arterial phase hyperenhancement, washout, and enhancing capsule. It outlines the dataset’s composition, curation process, and annotation pipeline following the Datasheets for Datasets format to promote transparency and reproducibility. The dataset aims to support the development of AI models for automated hepatocellular carcinoma diagnosis using the biopsy‑free, imaging‑based LI‑RADs framework.
By Pranav Kulkarni, Nikhil Shah, Amritansh Suryavanshi, Jana Delfino, James Tonascia, Jade Wong-You-Cheong, Barton Lane, Joseph Chirico, Jeffrey D. Hirsch, Ang Li, Heng Huang, Florence X. Doo
The paper presents AppendiGrade, an XAI‑enhanced deep learning framework that automatically detects complicated appendicitis from ultrasound images. Using a dataset of 4,679 images across five classes, the authors trained four pretrained models and achieved a best accuracy of 95.58% with InceptionV3 after applying preprocessing, hyperparameter tuning, and image sharpening. Grad‑CAM heatmaps were generated to explain the model’s predictions, facilitating easier expert cross‑checking.
By Fahad Ahammed, Omar Faruq Shikdar, Navid Zaman, Md Tahsin, Md. Nawab Yousuf Ali, Golam Sorwar