AutoSAM: Adapting SAM to Medical Images by Overloading the Prompt Encoder
Tal Shaharbany (Tel Aviv University),* Aviad Dahan (Tel Aviv University), Raja Giryes (Tel Aviv University), Lior Wolf (Tel Aviv University, Israel)
The 34th British Machine Vision Conference
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. Our code is publicly available at https://github.com/talshaharabany/AutoSAMVideo
Citation
@inproceedings{Shaharbany_2023_BMVC,
author = {Tal Shaharbany and Aviad Dahan and Raja Giryes and Lior Wolf},
title = {AutoSAM: Adapting SAM to Medical Images by Overloading the Prompt Encoder},
booktitle = {34th British Machine Vision Conference 2023, {BMVC} 2023, Aberdeen, UK, November 20-24, 2023},
publisher = {BMVA},
year = {2023},
url = {https://papers.bmvc2023.org/0530.pdf}
}
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