UniBiomed: A Universal Foundation Model for Grounded Biomedical Image Interpretation
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arXiv
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| Autori principali: | , , , , , , , , , , , , , , |
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| Natura: | Preprint |
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2025
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| author | Wu, Linshan Nie, Yuxiang He, Sunan Zhuang, Jiaxin Luo, Luyang Li, Tao Xie, Zhuoyao Chen, Dexuan Zhao, Yinghua Mahboobani, Neeraj Vardhanabhuti, Varut Chan, Ronald Cheong Kin Peng, Yifan Rajpurkar, Pranav Chen, Hao |
| author_facet | Wu, Linshan Nie, Yuxiang He, Sunan Zhuang, Jiaxin Luo, Luyang Li, Tao Xie, Zhuoyao Chen, Dexuan Zhao, Yinghua Mahboobani, Neeraj Vardhanabhuti, Varut Chan, Ronald Cheong Kin Peng, Yifan Rajpurkar, Pranav Chen, Hao |
| contents | The integration of AI-assisted biomedical image analysis into clinical practice demands AI-generated findings that are not only accurate but also interpretable to clinicians. However, existing biomedical AI models generally lack the ability to simultaneously generate diagnostic findings and localize corresponding biomedical objects. This limitation makes it challenging for clinicians to correlate AI-generated findings with visual evidence (e.g., tiny lesions) in images and interpret the results of AI models. To address this challenge, we introduce UniBiomed, the first universal foundation model for grounded biomedical image interpretation, which is capable of generating accurate diagnostic findings and simultaneously segmenting the corresponding biomedical targets. UniBiomed is based on a novel integration of Multi-modal Large Language Model and Segment Anything Model, which can effectively unify diverse biomedical tasks in universal training for advancing grounded interpretation. To develop UniBiomed, we curate a large-scale dataset comprising over 27 million triplets of images, region annotations, and text descriptions across ten biomedical imaging modalities. Extensive validation on 70 internal and 14 external datasets demonstrated the state-of-the-art performance of UniBiomed in diverse biomedical tasks, including image segmentation, disease recognition, region-aware diagnosis, vision question answering, and report generation. In summary, UniBiomed is a powerful and versatile biomedical foundation model, unlocking the untapped grounded interpretation capability for optimizing AI-assisted biomedical image analysis. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2504_21336 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | UniBiomed: A Universal Foundation Model for Grounded Biomedical Image Interpretation Wu, Linshan Nie, Yuxiang He, Sunan Zhuang, Jiaxin Luo, Luyang Li, Tao Xie, Zhuoyao Chen, Dexuan Zhao, Yinghua Mahboobani, Neeraj Vardhanabhuti, Varut Chan, Ronald Cheong Kin Peng, Yifan Rajpurkar, Pranav Chen, Hao Computer Vision and Pattern Recognition The integration of AI-assisted biomedical image analysis into clinical practice demands AI-generated findings that are not only accurate but also interpretable to clinicians. However, existing biomedical AI models generally lack the ability to simultaneously generate diagnostic findings and localize corresponding biomedical objects. This limitation makes it challenging for clinicians to correlate AI-generated findings with visual evidence (e.g., tiny lesions) in images and interpret the results of AI models. To address this challenge, we introduce UniBiomed, the first universal foundation model for grounded biomedical image interpretation, which is capable of generating accurate diagnostic findings and simultaneously segmenting the corresponding biomedical targets. UniBiomed is based on a novel integration of Multi-modal Large Language Model and Segment Anything Model, which can effectively unify diverse biomedical tasks in universal training for advancing grounded interpretation. To develop UniBiomed, we curate a large-scale dataset comprising over 27 million triplets of images, region annotations, and text descriptions across ten biomedical imaging modalities. Extensive validation on 70 internal and 14 external datasets demonstrated the state-of-the-art performance of UniBiomed in diverse biomedical tasks, including image segmentation, disease recognition, region-aware diagnosis, vision question answering, and report generation. In summary, UniBiomed is a powerful and versatile biomedical foundation model, unlocking the untapped grounded interpretation capability for optimizing AI-assisted biomedical image analysis. |
| title | UniBiomed: A Universal Foundation Model for Grounded Biomedical Image Interpretation |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2504.21336 |