arXiv AI By Yasaman Haghbin, Hadi Moradi, Reshad Hosseini

FICAug: Feature-Informed Clustering and Augmentation for Facial-Expression-Based Parkinson's Disease Screening

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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
arXiv Machine Learning
Jun 8

Explaining Unsupervised Disease Staging in Huntington's Disease: Insights into Model Representations and Clusters

arXiv:2606. 07135v1 Announce Type: new Abstract: Huntington's disease (HD) is a progressive neurodegenerative disorder that affects motor, cognitive, and behavioral functions, where accurate characterization of disease progression remains essential to improve patient outcome and quality of life.

By Lubna Mahmoud Abu Zohair, Hind Zantout
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.

arXiv Machine Learning
Jun 18

ActiTect: A Generalizable Machine Learning Pipeline for REM Sleep Behavior Disorder Screening through Standardized Actigraphy

arXiv:2511. 05221v3 Announce Type: replace Abstract: Isolated rapid eye movement sleep behavior disorder (iRBD) is a major prodromal marker of $\alpha$-synucleinopathies, often preceding the clinical onset of Parkinson's disease, dementia with Lewy bodies, or multiple system atrophy.

By David Bertram, Anja Ophey, Sinah R\"ottgen, Konstantin Kufer, Gereon R. Fink, Elke Kalbe, Clint Hansen, Walter Maetzler, Maximilian Kapsecker, Lara M. Reimer, Stephan Jonas, Andreas T. Damgaard, Natasha B. Bertelsen, Casper Skjaerbaek, Per Borghammer, Karolien Groenewald, Pietro-Luca Ratti, Michele T. Hu, No\'emie Moreau, Michael Sommerauer, Katarzyna Bozek