Foundation models for 3D medical segmentation lack true universality, study finds

A new perspective paper published on arXiv questions the current definition and validation of foundation models for 3D medical segmentation. The authors argue that despite promises of universal application, these models perform poorly on unseen data, particularly functional imaging modalities, indicating a significant gap between benchmark success and real-world clinical generalization. The paper calls for a reevaluation of how universality is defined and validated, advocating for broader testing across whole-body structural and functional imaging to bridge this gap and enable practical clinical translation. AI

IMPACT Highlights the need for more robust validation of AI models in critical applications like medical imaging.

RANK_REASON Research paper published on arXiv discussing limitations of foundation models. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.CV →

• 3D medical segmentation
• arXiv
• Foundation models
• Hugging Face
• Universality
• Yichi Zhang

• paper
• model release

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