Microsoft Health and Life Sciences is happy to announce the release of 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.
??On public datasets, MedImageInsight achieves SOTA in CT 3D medical image retrieval, as well as SOTA in disease classification and search for chest X-ray, dermatology, and OCT imaging. Furthermore, MedImageInsight achieves human expert performance in bone age estimation (on both public and partner data), as well as AUC above 0.9 in most other domains.
?? When paired with a text decoder, MedImageInsight achieves near SOTA level single image report findings generation with less than 10% the parameters of other models. Compared to fine-tuning GPT-4o with only MIMIC-CXR data for the same task, MedImageInsight outperforms in clinical metrics, but underperforms on lexical metrics where GPT-4o sets a new SOTA.
??Importantly for regulatory purposes, MedImageInsight can generate ROC curves, adjust sensitivity and specificity based on clinical need, and provide evidence-based decision support through image-image search (which can also enable retrieval augmented generation).
??In an independent clinical evaluation of image-image search in chest X-ray, MedImageInsight outperformed every other publicly available foundation model evaluated by large margins (over 6 points AUC), and significantly outperformed other models in terms of AI fairness (across age and gender).
We hope releasing MedImageInsight (with both open source research and commercial license) will help enhance collective progress in medical imaging AI research and development.
This work was made possible by the tireless efforts of a dedicated team across multiple departments and organizations:??Noel C. F. Codella, Ph.D., Ying Jin, Shrey J., Yu Gu, Peter Lee, Dr. Asma Ben Abacha, Santamaria-Pang Alberto, Will Guyman, Naiteek Sangani, Sheng Zhang, Hoifung Poon, Stephanie Hyland, Shruthi Bannur, Javier Alvarez Valle, Xue Li, John Garrett, Alan McMillan, Gaurav Rajguru, Madhu Maddi, Nilesh Vijayrania, Rehaan Bhimani, Nick Mecklenburg, Rupal Jain, Daniel Holstein, Naveen Gaur, Vijay Aski, Hwang, Jenq-Neng, Thomas Lin, Ivan Tarapov, Matthew Lungren MD MPH, Mu Wei
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