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arXiv · 2306.06370

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

Tal Shaharabany·Aviad Dahan·Raja Giryes·Lior Wolf
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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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Tal Shaharabany, Aviad Dahan, Raja Giryes, Lior Wolf. 2023-06-10. AutoSAM: Adapting SAM to Medical Images by Overloading the Prompt Encoder. https://arxiv.org/abs/2306.06370

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