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The recently introduced Segment Anything Model (SAM) combines a cleverarchitecture and large quantities of training data to obtain remarkable imagesegmentation capabilities. However, it fails to reproduce such results forOut-Of-Distribution (OOD) domains such as medical images. Moreover, while SAMis conditioned on either a mask or a set of points, it may be desirable to havea fully automatic solution. In this work, we replace SAM's conditioning with anencoder that operates on the same input image. By adding this encoder andwithout further fine-tuning SAM, we obtain state-of-the-art results on multiplemedical images and video benchmarks. This new encoder is trained via gradientsprovided by a frozen SAM. For inspecting the knowledge within it, and providinga lightweight segmentation solution, we also learn to decode it into a mask bya shallow deconvolution network.

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