AutoSAM: Adapting SAM to Medical Images by Overloading the Prompt Encoder

• URL: http://arxiv.org/abs/2306.06370v1
• Date: Sat, 10 Jun 2023 07:27:00 GMT
• Title: AutoSAM: Adapting SAM to Medical Images by Overloading the Prompt Encoder
• Authors: Tal Shaharabany, Aviad Dahan, Raja Giryes and Lior Wolf
• Abstract summary: In this work, we replace Segment Anything Model with an encoder that operates on the same input image. We obtain state-of-the-art results on multiple medical images and video benchmarks. For inspecting the knowledge within it, and providing a lightweight segmentation solution, we also learn to decode it into a mask by a shallow deconvolution network.
• Score: 101.28268762305916
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: The recently introduced Segment Anything Model (SAM) combines a clever architecture and large quantities of training data to obtain remarkable image segmentation capabilities. However, it fails to reproduce such results for Out-Of-Distribution (OOD) domains such as medical images. Moreover, while SAM is conditioned on either a mask or a set of points, it may be desirable to have a fully automatic solution. In this work, we replace SAM's conditioning with an encoder that operates on the same input image. By adding this encoder and without further fine-tuning SAM, we obtain state-of-the-art results on multiple medical images and video benchmarks. This new encoder is trained via gradients provided by a frozen SAM. For inspecting the knowledge within it, and providing a lightweight segmentation solution, we also learn to decode it into a mask by a shallow deconvolution network.

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