Medical SAM adapter: Adapting segment anything model for medical image segmentation

The Segment Anything Model (SAM) has recently gained popularity in the field of image segmentation due to its impressive capabilities in various segmentation tasks and its prompt-based interface. However, recent studies and individual experiments have shown that SAM underperforms in medical image se...

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Veröffentlicht in:	Medical image analysis Jg. 102; S. 103547
Hauptverfasser:	Wu, Junde, Wang, Ziyue, Hong, Mingxuan, Ji, Wei, Fu, Huazhu, Xu, Yanwu, Xu, Min, Jin, Yueming
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	Netherlands Elsevier B.V 01.05.2025
Schlagworte:	Algorithms Efficient Learning Fine-Tuning Humans Image Interpretation, Computer-Assisted - methods Image Processing, Computer-Assisted - methods Imaging, Three-Dimensional - methods Medical Image Segmentation Efficient Learning Fine-Tuning Medical Image Segmentation 41A10 65D05 65D17 41A05
ISSN:	1361-8415, 1361-8423, 1361-8423
Online-Zugang:	Volltext
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