Parameter Efficient Fine-Tuning of Segment Anything Model for Biomedical Imaging
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arXiv
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| Auteurs principaux: | , , |
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| Format: | Preprint |
| Publié: |
2025
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| _version_ | 1866916771130245120 |
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| author | Teuber, Carolin Archit, Anwai Pape, Constantin |
| author_facet | Teuber, Carolin Archit, Anwai Pape, Constantin |
| contents | Segmentation is an important analysis task for biomedical images, enabling the study of individual organelles, cells or organs. Deep learning has massively improved segmentation methods, but challenges remain in generalization to new conditions, requiring costly data annotation. Vision foundation models, such as Segment Anything Model (SAM), address this issue through improved generalization. However, these models still require finetuning on annotated data, although with less annotations, to achieve optimal results for new conditions. As a downside, they require more computational resources. This makes parameter-efficient finetuning (PEFT) relevant. We contribute the first comprehensive study of PEFT for SAM applied to biomedical images. We find that the placement of PEFT layers is more important for efficiency than the type of layer for vision transformers and we provide a recipe for resource-efficient finetuning. Our code is publicly available at https://github.com/computational-cell-analytics/peft-sam. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2502_00418 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Parameter Efficient Fine-Tuning of Segment Anything Model for Biomedical Imaging Teuber, Carolin Archit, Anwai Pape, Constantin Computer Vision and Pattern Recognition Segmentation is an important analysis task for biomedical images, enabling the study of individual organelles, cells or organs. Deep learning has massively improved segmentation methods, but challenges remain in generalization to new conditions, requiring costly data annotation. Vision foundation models, such as Segment Anything Model (SAM), address this issue through improved generalization. However, these models still require finetuning on annotated data, although with less annotations, to achieve optimal results for new conditions. As a downside, they require more computational resources. This makes parameter-efficient finetuning (PEFT) relevant. We contribute the first comprehensive study of PEFT for SAM applied to biomedical images. We find that the placement of PEFT layers is more important for efficiency than the type of layer for vision transformers and we provide a recipe for resource-efficient finetuning. Our code is publicly available at https://github.com/computational-cell-analytics/peft-sam. |
| title | Parameter Efficient Fine-Tuning of Segment Anything Model for Biomedical Imaging |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2502.00418 |