MedSapiens: Taking a Pose to Rethink Medical Imaging Landmark Detection

Marawan Elbatel; Anbang Wang; Keyuan Liu; Kaouther Mouheb; Enrique Almar-Munoz; Lizhuo Lin; Yanqi Yang; Karim Lekadir; Xiaomeng Li

arXiv · 2511.04255

MedSapiens: Taking a Pose to Rethink Medical Imaging Landmark Detection

Marawan Elbatel·Anbang Wang·Keyuan Liu·Kaouther Mouheb·Enrique Almar-Munoz·Lizhuo Lin·Yanqi Yang·Karim Lekadir·Xiaomeng Li

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Abstract

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 .

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BibTeX RIS

Marawan Elbatel, Anbang Wang, Keyuan Liu, Kaouther Mouheb, Enrique Almar-Munoz, Lizhuo Lin, Yanqi Yang, Karim Lekadir, Xiaomeng Li. 2025-11-06. MedSapiens: Taking a Pose to Rethink Medical Imaging Landmark Detection. https://arxiv.org/abs/2511.04255

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