MedSapiens: Taking a Pose to Rethink Medical Imaging Landmark Detection
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arXiv
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| Main Authors: | , , , , , , , , |
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| Format: | Preprint |
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2025
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| _version_ | 1866908633141346304 |
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| author | Elbatel, Marawan Wang, Anbang Liu, Keyuan Mouheb, Kaouther Almar-Munoz, Enrique Lin, Lizhuo Yang, Yanqi Lekadir, Karim Li, Xiaomeng |
| author_facet | Elbatel, Marawan Wang, Anbang Liu, Keyuan Mouheb, Kaouther Almar-Munoz, Enrique Lin, Lizhuo Yang, Yanqi Lekadir, Karim Li, Xiaomeng |
| contents | This paper does not introduce a novel architecture; instead, it revisits a fundamental yet overlooked baseline: adapting human-centric foundation models for anatomical landmark detection in medical imaging. While landmark detection has traditionally relied on domain-specific models, the emergence of large-scale pre-trained vision models presents new opportunities. In this study, we investigate the adaptation of Sapiens, a human-centric foundation model designed for pose estimation, to medical imaging through multi-dataset pretraining, establishing a new state of the art across multiple datasets. Our proposed model, MedSapiens, demonstrates that human-centric foundation models, inherently optimized for spatial pose localization, provide strong priors for anatomical landmark detection, yet this potential has remained largely untapped. We benchmark MedSapiens against existing state-of-the-art models, achieving up to 5.26% improvement over generalist models and up to 21.81% improvement over specialist models in the average success detection rate (SDR). To further assess MedSapiens adaptability to novel downstream tasks with few annotations, we evaluate its performance in limited-data settings, achieving 2.69% improvement over the few-shot state of the art in SDR. Code and model weights are available at https://github.com/xmed-lab/MedSapiens . |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2511_04255 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | MedSapiens: Taking a Pose to Rethink Medical Imaging Landmark Detection Elbatel, Marawan Wang, Anbang Liu, Keyuan Mouheb, Kaouther Almar-Munoz, Enrique Lin, Lizhuo Yang, Yanqi Lekadir, Karim Li, Xiaomeng Computer Vision and Pattern Recognition Artificial Intelligence Machine Learning This paper does not introduce a novel architecture; instead, it revisits a fundamental yet overlooked baseline: adapting human-centric foundation models for anatomical landmark detection in medical imaging. While landmark detection has traditionally relied on domain-specific models, the emergence of large-scale pre-trained vision models presents new opportunities. In this study, we investigate the adaptation of Sapiens, a human-centric foundation model designed for pose estimation, to medical imaging through multi-dataset pretraining, establishing a new state of the art across multiple datasets. Our proposed model, MedSapiens, demonstrates that human-centric foundation models, inherently optimized for spatial pose localization, provide strong priors for anatomical landmark detection, yet this potential has remained largely untapped. We benchmark MedSapiens against existing state-of-the-art models, achieving up to 5.26% improvement over generalist models and up to 21.81% improvement over specialist models in the average success detection rate (SDR). To further assess MedSapiens adaptability to novel downstream tasks with few annotations, we evaluate its performance in limited-data settings, achieving 2.69% improvement over the few-shot state of the art in SDR. Code and model weights are available at https://github.com/xmed-lab/MedSapiens . |
| title | MedSapiens: Taking a Pose to Rethink Medical Imaging Landmark Detection |
| topic | Computer Vision and Pattern Recognition Artificial Intelligence Machine Learning |
| url | https://arxiv.org/abs/2511.04255 |