References & Citations
Computer Science > Computer Vision and Pattern Recognition
Title: Medical SAM Adapter: Adapting Segment Anything Model for Medical Image Segmentation
(Submitted on 25 Apr 2023 (v1), last revised 29 Dec 2023 (this version, v7))
Abstract: The Segment Anything Model (SAM) has recently gained popularity in the field of image segmentation due to its impressive capabilities in various segmentation tasks and its prompt-based interface. However, recent studies and individual experiments have shown that SAM underperforms in medical image segmentation, since the lack of the medical specific knowledge. This raises the question of how to enhance SAM's segmentation capability for medical images. In this paper, instead of fine-tuning the SAM model, we propose the Medical SAM Adapter (Med-SA), which incorporates domain-specific medical knowledge into the segmentation model using a light yet effective adaptation technique. In Med-SA, we propose Space-Depth Transpose (SD-Trans) to adapt 2D SAM to 3D medical images and Hyper-Prompting Adapter (HyP-Adpt) to achieve prompt-conditioned adaptation. We conduct comprehensive evaluation experiments on 17 medical image segmentation tasks across various image modalities. Med-SA outperforms several state-of-the-art (SOTA) medical image segmentation methods, while updating only 2\% of the parameters. Our code is released at this https URL
Submission history
From: Junde Wu [view email][v1] Tue, 25 Apr 2023 07:34:22 GMT (10845kb,D)
[v2] Wed, 26 Apr 2023 13:20:17 GMT (10842kb,D)
[v3] Wed, 3 May 2023 08:25:22 GMT (10842kb,D)
[v4] Thu, 4 May 2023 04:03:33 GMT (10842kb,D)
[v5] Thu, 11 May 2023 12:07:35 GMT (10842kb,D)
[v6] Sat, 13 May 2023 08:00:39 GMT (10842kb,D)
[v7] Fri, 29 Dec 2023 03:40:59 GMT (8275kb,D)
Link back to: arXiv, form interface, contact.