标题： ADAptation：基于重建的无监督主动学习用于乳腺超声诊断 显示英文标题

标题： ADAptation: Reconstruction-based Unsupervised Active Learning for Breast Ultrasound Diagnosis

摘要： 基于深度学习的诊断模型经常由于训练（源）域和测试（目标）域之间的分布偏移而出现性能下降。 为模型重新训练收集和标记足够的目标域数据是一种最佳解决方案，但受到时间和稀缺资源的限制。 主动学习（AL）提供了一种有效的方法，在保持性能的同时减少注释成本，但难以应对不同数据集之间分布变化带来的挑战。 在本研究中，我们提出了一种新的无监督主动学习框架用于领域适应，命名为 ADAptation，该框架在有限的注释预算下能够从多领域数据池中高效选择有信息量的样本。 作为基础步骤，我们的方法首先利用扩散模型的分布同质化能力，通过将目标图像转换为目标域风格来弥合跨数据集的差距。 我们随后引入了两个关键创新：(a) 一种超球体约束对比学习网络，用于紧凑特征聚类，以及 (b) 一种双评分机制，用于量化和平衡样本的不确定性和代表性。 在四个乳腺超声数据集（三个公开数据集和一个内部/多中心数据集）上进行的广泛实验，涉及五种常见的深度分类器，证明了我们的方法优于现有的强大 AL 基础竞争对手，验证了其在临床领域适应中的有效性和泛化性。 代码可在匿名链接中获得：https://github.com/miccai25-966/ADAptation。 显示英文摘要

摘要： Deep learning-based diagnostic models often suffer performance drops due to distribution shifts between training (source) and test (target) domains. Collecting and labeling sufficient target domain data for model retraining represents an optimal solution, yet is limited by time and scarce resources. Active learning (AL) offers an efficient approach to reduce annotation costs while maintaining performance, but struggles to handle the challenge posed by distribution variations across different datasets. In this study, we propose a novel unsupervised Active learning framework for Domain Adaptation, named ADAptation, which efficiently selects informative samples from multi-domain data pools under limited annotation budget. As a fundamental step, our method first utilizes the distribution homogenization capabilities of diffusion models to bridge cross-dataset gaps by translating target images into source-domain style. We then introduce two key innovations: (a) a hypersphere-constrained contrastive learning network for compact feature clustering, and (b) a dual-scoring mechanism that quantifies and balances sample uncertainty and representativeness. Extensive experiments on four breast ultrasound datasets (three public and one in-house/multi-center) across five common deep classifiers demonstrate that our method surpasses existing strong AL-based competitors, validating its effectiveness and generalization for clinical domain adaptation. The code is available at the anonymized link: https://github.com/miccai25-966/ADAptation.