Saved in:
| Main Authors: | , , , , , , , , , , , , , , , , , , , , , , , , |
|---|---|
| Format: | Preprint |
| Published: |
2025
|
| Subjects: | |
| Online Access: | https://arxiv.org/abs/2507.00185 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1866915366510264320 |
|---|---|
| author | Zhou, Yang Quek, Chrystie Wan Ning Zhou, Jun Wang, Yan Bai, Yang Ke, Yuhe Yao, Jie Gutierrez, Laura Teo, Zhen Ling Ting, Darren Shu Jeng Soetikno, Brian T. Nielsen, Christopher S. Elze, Tobias Li, Zengxiang Dinh, Linh Le Cheng, Lionel Tim-Ee Anh, Tran Nguyen Tuan Cheng, Chee Leong Wong, Tien Yin Liu, Nan Tan, Iain Beehuat Lim, Tony Kiat Hon Goh, Rick Siow Mong Liu, Yong Ting, Daniel Shu Wei |
| author_facet | Zhou, Yang Quek, Chrystie Wan Ning Zhou, Jun Wang, Yan Bai, Yang Ke, Yuhe Yao, Jie Gutierrez, Laura Teo, Zhen Ling Ting, Darren Shu Jeng Soetikno, Brian T. Nielsen, Christopher S. Elze, Tobias Li, Zengxiang Dinh, Linh Le Cheng, Lionel Tim-Ee Anh, Tran Nguyen Tuan Cheng, Chee Leong Wong, Tien Yin Liu, Nan Tan, Iain Beehuat Lim, Tony Kiat Hon Goh, Rick Siow Mong Liu, Yong Ting, Daniel Shu Wei |
| contents | Current artificial intelligence models for medical imaging are predominantly single modality and single disease. Attempts to create multimodal and multi-disease models have resulted in inconsistent clinical accuracy. Furthermore, training these models typically requires large, labour-intensive, well-labelled datasets. We developed MerMED-FM, a state-of-the-art multimodal, multi-specialty foundation model trained using self-supervised learning and a memory module. MerMED-FM was trained on 3.3 million medical images from over ten specialties and seven modalities, including computed tomography (CT), chest X-rays (CXR), ultrasound (US), pathology patches, color fundus photography (CFP), optical coherence tomography (OCT) and dermatology images. MerMED-FM was evaluated across multiple diseases and compared against existing foundational models. Strong performance was achieved across all modalities, with AUROCs of 0.988 (OCT); 0.982 (pathology); 0.951 (US); 0.943 (CT); 0.931 (skin); 0.894 (CFP); 0.858 (CXR). MerMED-FM has the potential to be a highly adaptable, versatile, cross-specialty foundation model that enables robust medical imaging interpretation across diverse medical disciplines. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2507_00185 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Multimodal, Multi-Disease Medical Imaging Foundation Model (MerMED-FM) Zhou, Yang Quek, Chrystie Wan Ning Zhou, Jun Wang, Yan Bai, Yang Ke, Yuhe Yao, Jie Gutierrez, Laura Teo, Zhen Ling Ting, Darren Shu Jeng Soetikno, Brian T. Nielsen, Christopher S. Elze, Tobias Li, Zengxiang Dinh, Linh Le Cheng, Lionel Tim-Ee Anh, Tran Nguyen Tuan Cheng, Chee Leong Wong, Tien Yin Liu, Nan Tan, Iain Beehuat Lim, Tony Kiat Hon Goh, Rick Siow Mong Liu, Yong Ting, Daniel Shu Wei Image and Video Processing Artificial Intelligence Computer Vision and Pattern Recognition Current artificial intelligence models for medical imaging are predominantly single modality and single disease. Attempts to create multimodal and multi-disease models have resulted in inconsistent clinical accuracy. Furthermore, training these models typically requires large, labour-intensive, well-labelled datasets. We developed MerMED-FM, a state-of-the-art multimodal, multi-specialty foundation model trained using self-supervised learning and a memory module. MerMED-FM was trained on 3.3 million medical images from over ten specialties and seven modalities, including computed tomography (CT), chest X-rays (CXR), ultrasound (US), pathology patches, color fundus photography (CFP), optical coherence tomography (OCT) and dermatology images. MerMED-FM was evaluated across multiple diseases and compared against existing foundational models. Strong performance was achieved across all modalities, with AUROCs of 0.988 (OCT); 0.982 (pathology); 0.951 (US); 0.943 (CT); 0.931 (skin); 0.894 (CFP); 0.858 (CXR). MerMED-FM has the potential to be a highly adaptable, versatile, cross-specialty foundation model that enables robust medical imaging interpretation across diverse medical disciplines. |
| title | Multimodal, Multi-Disease Medical Imaging Foundation Model (MerMED-FM) |
| topic | Image and Video Processing Artificial Intelligence Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2507.00185 |