Prompt-Free and Efficient SAM2 Adaptation for Biomedical Semantic Segmentation via Dual Adapters
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arXiv
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| Autori principali: | , |
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| Natura: | Preprint |
| Pubblicazione: |
2026
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| _version_ | 1866909021390241792 |
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| author | Mitsuoka, Hinako Hotta, Kazuhiro |
| author_facet | Mitsuoka, Hinako Hotta, Kazuhiro |
| contents | Segment Anything Model 2 (SAM2) demonstrated impressive zero-shot capabilities on natural images but faces challenges in biomedical segmentation due to significant domain shifts and prompt dependency. To address these limitations, we propose a prompt-free, parameter-efficient fine-tuning framework designed for multi-class segmentation on variable-sized inputs. We introduce a convolutional Positional Encoding Generator to adapt effectively to arbitrary aspect ratios and present a dual-adapter strategy: High-Performance Adapter utilizing deformable convolutions for precise boundary modeling and Lightweight Adapter employing structural re-parameterization to minimize inference latency. Experiments on ISBI 2012, Kvasir-SEG, Synapse, and ACDC datasets demonstrate that our approach significantly outperforms strong adaptation baselines. Specifically, our method improved segmentation accuracy by up to 19.66\% over the vanilla SAM2, while reducing computational costs by approximately 87\% compared to heavyweight medical SAM adaptations, establishing a superior trade-off between accuracy and efficiency. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2605_05979 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Prompt-Free and Efficient SAM2 Adaptation for Biomedical Semantic Segmentation via Dual Adapters Mitsuoka, Hinako Hotta, Kazuhiro Computer Vision and Pattern Recognition Segment Anything Model 2 (SAM2) demonstrated impressive zero-shot capabilities on natural images but faces challenges in biomedical segmentation due to significant domain shifts and prompt dependency. To address these limitations, we propose a prompt-free, parameter-efficient fine-tuning framework designed for multi-class segmentation on variable-sized inputs. We introduce a convolutional Positional Encoding Generator to adapt effectively to arbitrary aspect ratios and present a dual-adapter strategy: High-Performance Adapter utilizing deformable convolutions for precise boundary modeling and Lightweight Adapter employing structural re-parameterization to minimize inference latency. Experiments on ISBI 2012, Kvasir-SEG, Synapse, and ACDC datasets demonstrate that our approach significantly outperforms strong adaptation baselines. Specifically, our method improved segmentation accuracy by up to 19.66\% over the vanilla SAM2, while reducing computational costs by approximately 87\% compared to heavyweight medical SAM adaptations, establishing a superior trade-off between accuracy and efficiency. |
| title | Prompt-Free and Efficient SAM2 Adaptation for Biomedical Semantic Segmentation via Dual Adapters |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2605.05979 |