We present a comprehensive evaluation of Parameter-Efficient Fine-Tuning (PEFT) techniques for diverse medical image analysis tasks. PEFT is increasingly exploited as a valuable approach for knowledge transfer from pre-trained models in natural language processing, vision, speech, and cross-modal tasks, such as vision-language and text-to-image generation. However, its application in medical image analysis remains relatively unexplored. As foundation models are increasingly exploited in the medical domain, it is crucial to investigate and comparatively assess various strategies for knowledge transfer that can bolster a range of downstream tasks. Our study, the first of its kind (to the best of our knowledge), evaluates 16 distinct PEFT methodologies proposed for convolutional and transformer-based networks, focusing on image classification and text-to-image generation tasks across six medical datasets ranging in size, modality, and complexity. Through a battery of more than 600 controlled experiments, we demonstrate performance gains of up to 22% under certain scenarios and demonstrate the efficacy of PEFT for medical text-to-image generation. Further, we reveal the instances where PEFT methods particularly dominate over conventional fine-tuning approaches by studying their relationship with downstream data volume.

翻译：我们针对各类医学图像分析任务，系统评估了参数高效微调（PEFT）技术。在自然语言处理、视觉、语音以及跨模态任务（如视觉-语言和文本到图像生成）中，PEFT 被日益广泛地用作从预训练模型迁移知识的有效方法。然而，其在医学图像分析领域的应用仍相对未被充分探索。随着基础模型在医学领域的应用日益普及，探讨和比较评估各种能够增强下游任务的知识迁移策略至关重要。据我们所知，本研究首次评估了16种针对卷积网络和基于Transformer网络提出的不同PEFT方法，重点聚焦于六种涵盖不同规模、模态及复杂度的医学数据集上的图像分类与文本到图像生成任务。通过超过600次受控实验，我们证明了在某些场景下性能提升可达22%，并验证了PEFT在医学文本到图像生成中的有效性。此外，通过研究PEFT方法与下游数据量的关系，我们揭示了PEFT方法在哪些情形下特别优于传统微调方法。