Segment anything in medical images

Medical image segmentation is a critical component in clinical practice, facilitating accurate diagnosis, treatment planning, and disease monitoring. We conduct a comprehensive evaluation on 86 internal validation tasks and 60 external validation tasks, demonstrating better accuracy and robustness than modality-wise specialist models.

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• 1 However, existing methods, often tailored to specific modalities or disease types, lack generalizability across the diverse spectrum of medical image segmentation tasks.
• 2 Here we present MedSAM, a foundation model designed for bridging this gap by enabling universal medical image segmentation.
• 3 The model is developed on a large-scale medical image dataset with 1,570,263 image-mask pairs, covering 10 imaging modalities and over 30 cancer types.

Understanding this could lead to better treatments, improved diagnostics, or a deeper grasp of how the human body works — benefiting patient care globally.

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