Efficient Self-Supervised Adaptation for Medical Image Analysis
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arXiv
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| Main Authors: | , , , , , |
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866911180382011392 |
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| author | Sorkhei, Moein Konuk, Emir Guo, Jingyu Meng, Chanjuan Matsoukas, Christos Smith, Kevin |
| author_facet | Sorkhei, Moein Konuk, Emir Guo, Jingyu Meng, Chanjuan Matsoukas, Christos Smith, Kevin |
| contents | Self-supervised adaptation (SSA) improves foundation model transfer to medical domains but is computationally prohibitive. Although parameter efficient fine-tuning methods such as LoRA have been explored for supervised adaptation, their effectiveness for SSA remains unknown. In this work, we introduce efficient self-supervised adaptation (ESSA), a framework that applies parameter-efficient fine-tuning techniques to SSA with the aim of reducing computational cost and improving adaptation performance. Among the methods tested, Attention Projection Layer Adaptation (APLA) sets a new state-of-the-art, consistently surpassing full-parameter SSA and supervised fine-tuning across diverse medical tasks, while reducing GPU memory by up to 40.1% and increasing training throughput by 25.2%, all while maintaining inference efficiency. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2503_18873 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Efficient Self-Supervised Adaptation for Medical Image Analysis Sorkhei, Moein Konuk, Emir Guo, Jingyu Meng, Chanjuan Matsoukas, Christos Smith, Kevin Computer Vision and Pattern Recognition Self-supervised adaptation (SSA) improves foundation model transfer to medical domains but is computationally prohibitive. Although parameter efficient fine-tuning methods such as LoRA have been explored for supervised adaptation, their effectiveness for SSA remains unknown. In this work, we introduce efficient self-supervised adaptation (ESSA), a framework that applies parameter-efficient fine-tuning techniques to SSA with the aim of reducing computational cost and improving adaptation performance. Among the methods tested, Attention Projection Layer Adaptation (APLA) sets a new state-of-the-art, consistently surpassing full-parameter SSA and supervised fine-tuning across diverse medical tasks, while reducing GPU memory by up to 40.1% and increasing training throughput by 25.2%, all while maintaining inference efficiency. |
| title | Efficient Self-Supervised Adaptation for Medical Image Analysis |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2503.18873 |