Medical image analysis using deep learning is often challenged by limited labeled data and high annotation costs. Fine-tuning the entire network in label-limited scenarios can lead to overfitting and suboptimal performance. Recently, prompt tuning has emerged as a more promising technique that introduces a few additional tunable parameters as prompts to a task-agnostic pre-trained model, and updates only these parameters using supervision from limited labeled data while keeping the pre-trained model unchanged. However, previous work has overlooked the importance of selective labeling in downstream tasks, which aims to select the most valuable downstream samples for annotation to achieve the best performance with minimum annotation cost. To address this, we propose a framework that combines selective labeling with prompt tuning (SLPT) to boost performance in limited labels. Specifically, we introduce a feature-aware prompt updater to guide prompt tuning and a TandEm Selective LAbeling (TESLA) strategy. TESLA includes unsupervised diversity selection and supervised selection using prompt-based uncertainty. In addition, we propose a diversified visual prompt tuning strategy to provide multi-prompt-based discrepant predictions for TESLA. We evaluate our method on liver tumor segmentation and achieve state-of-the-art performance, outperforming traditional fine-tuning with only 6% of tunable parameters, also achieving 94% of full-data performance by labeling only 5% of the data.

翻译：使用深度学习进行医学图像分析常面临标注数据有限和标注成本高昂的挑战。在标签受限场景中，微调整个网络会导致过拟合和次优性能。最近，提示调优作为一种更有前景的技术出现，它向任务无关的预训练模型引入少量可调节参数作为提示，仅利用有限标注数据的监督更新这些参数，同时保持预训练模型不变。然而，以往研究忽略了下游任务中选择性标注的重要性——该技术旨在选择最有价值的下游样本进行标注，以最小标注成本实现最优性能。为此，我们提出一种将选择性标注与提示调优相结合（SLPT）的框架，以提升有限标签下的性能。具体而言，我们引入特征感知型提示更新器指导提示调优，并设计了一种"串联式选择性标注"（TESLA）策略。TESLA包含基于无监督的多样性选择方法和基于提示不确定性的监督选择方法。此外，我们提出多样化视觉提示调优策略，为TESLA提供基于多提示的差异性预测。我们在肝脏肿瘤分割任务上评估该方法，仅使用6%的可调节参数即取得超越传统微调的最优性能，并通过标注5%的数据实现了全量数据性能的94%。