# SAM-DA: Decoder Adapter for Efficient Medical Domain Adaptation Source: [SAM-DA: Decoder Adapter for Efficient Medical Domain Adaptation]() Collection index: [llms.txt]() Data updated: 2025-10-26T03:56:26.552Z Authors: Javier Gamazo Tejero, Moritz Schmid, Pablo M\\'arquez Neila, Martin S. Zinkernagel, Sebastian Wolf, Raphael Sznitman Published: 2025-01-12T15:08:29.000Z ## Abstract This paper addresses the domain adaptation challenge for semantic segmentation in medical imaging. Despite the impressive performance of recent foundational segmentation models like SAM on natural images, they struggle with medical domain images. Beyond this, recent approaches that perform end-to-end fine-tuning of models are simply not computationally tractable. To address this, we propose a novel SAM adapter approach that minimizes the number of trainable parameters while achieving comparable performances to full fine-tuning. The proposed SAM adapter is strategically placed in the mask decoder, offering excellent and broad generalization capabilities and improved segmentation across both fully supervised and test-time domain adaptation tasks. Extensive validation on four datasets showcases the adapter's efficacy, outperforming existing methods while training less than 1% of SAM's total parameters. ## Recorded benchmark results A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage. ## Original sources and implementation [Original paper]() [PDF]()