{"paper":{"title":"MedImageInsight: An Open-Source Embedding Model for General Domain Medical Imaging","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.CV"],"primary_cat":"eess.IV","authors_text":"Alan McMillan, Alberto Santamaria-Pang, Asma Ben Abacha, Daniel Holstein, Gaurav Rajguru, Ho Hin Lee, Hoifung Poon, Ivan Tarapov, Javier Alvarez-Valle, Jenq-Neng Hwang, John Garrett, Madhu Maddi, Matthew Lungren, Mu Wei, Naiteek Sangani, Naveen Gaur, Nick Mecklenburg, Nilesh Vijayrania, Noel C. F. Codella, Rehaan Bhimai, Rupal Jain, Sheng Zhang, Shrey Jain, Shruthi Bannur, Stephanie Hyland, Thomas Lin, Vijay Aski, Will Guyman, Xue Li, Ying Jin, Yu Gu","submitted_at":"2024-10-09T04:36:47Z","abstract_excerpt":"In this work, we present MedImageInsight, an open-source medical imaging embedding model. MedImageInsight is trained on medical images with associated text and labels across a diverse collection of domains, including X-Ray, CT, MRI, dermoscopy, OCT, fundus photography, ultrasound, histopathology, and mammography. Rigorous evaluations demonstrate MedImageInsight's ability to achieve state-of-the-art (SOTA) or human expert level performance across classification, image-image search, and fine-tuning tasks. Specifically, on public datasets, MedImageInsight achieves SOTA in CT 3D medical image retr"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2410.06542","kind":"arxiv","version":1},"verdict":{"id":null,"model_set":{},"created_at":null,"strongest_claim":"","one_line_summary":"","pipeline_version":null,"weakest_assumption":"","pith_extraction_headline":""},"integrity":{"clean":true,"summary":{"advisory":0,"critical":0,"by_detector":{},"informational":0},"endpoint":"/pith/2410.06542/integrity.json","findings":[],"available":true,"detectors_run":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938"},"references":{"count":0,"sample":[],"resolved_work":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57","internal_anchors":0},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"author_claims":{"count":0,"strong_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"builder_version":"pith-number-builder-2026-05-17-v1"}