提出高效自监督适配框架，显著降低医疗图像分析的计算开销。
Efficient Self-Supervised Adaptation for Medical Image Analysis
- 采用参数高效微调技术改进自监督适配流程
- 在多任务医疗场景中超越全参数训练，内存减少40.1%
- 适合需要低资源部署的医疗AI研究与应用
自监督适配（SSA）虽能提升基础模型在医疗领域的迁移效果，但计算成本高昂。尽管参数高效的微调方法如LoRA已在监督学习中被探索，其在自监督适配中的有效性尚不明确。本文提出高效自监督适配（ESSA）框架，将参数高效微调技术应用于SSA，旨在降低计算开销并提升适配性能。实验表明，注意力投影层适配（APLA）达到新最佳表现，在多种医疗任务中持续优于全参数SSA和监督微调，同时将GPU内存使用减少最多达40.1%，训练吞吐量提升25.2%，且保持推理效率。
原文摘要 · Abstract (English)
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.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。