March 28, 2024, 4:45 a.m. | Yihao Liu, Jiaming Zhang, Andres Diaz-Pinto, Haowei Li, Alejandro Martin-Gomez, Amir Kheradmand, Mehran Armand

cs.CV updates on arXiv.org arxiv.org

arXiv:2403.18114v1 Announce Type: new
Abstract: The Segment Anything Model (SAM) has drawn significant attention from researchers who work on medical image segmentation because of its generalizability. However, researchers have found that SAM may have limited performance on medical images compared to state-of-the-art non-foundation models. Regardless, the community sees potential in extending, fine-tuning, modifying, and evaluating SAM for analysis of medical imaging. An increasing number of works have been published focusing on the mentioned four directions, where variants of SAM are …

abstract art arxiv attention community cs.cv fine-tuning found foundation however image images medical performance researchers sam segment segment anything segment anything model segmentation state type work

Founding AI Engineer, Agents

@ Occam AI | New York

AI Engineer Intern, Agents

@ Occam AI | US

AI Research Scientist

@ Vara | Berlin, Germany and Remote

Data Architect

@ University of Texas at Austin | Austin, TX

Data ETL Engineer

@ University of Texas at Austin | Austin, TX

Consultant Senior Power BI & Azure - CDI - H/F

@ Talan | Lyon, France