FSAM integrates a frequency adapter into SAM with LoRA to extract domain-invariant high-frequency features and outperforms prior domain generalization methods on fundus and prostate datasets.
In: Medical image computing and computer-assisted intervention–MICCAI 2015: 18th international conference, Munich, Germany, Oc- tober 5-9
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GSAM applies random cropping to enable variable input sizes for efficient SAM fine-tuning, claiming lower compute with comparable or higher accuracy on varied datasets.
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Frequency Adapter with SAM for Generalized Medical Image Segmentation
FSAM integrates a frequency adapter into SAM with LoRA to extract domain-invariant high-frequency features and outperforms prior domain generalization methods on fundus and prostate datasets.
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Generalized SAM: Efficient Fine-Tuning of SAM for Variable Input Image Sizes
GSAM applies random cropping to enable variable input sizes for efficient SAM fine-tuning, claiming lower compute with comparable or higher accuracy on varied datasets.