Deep learning based medical image diagnosis has shown great potential in clinical medicine. However, it often suffers two major difficulties in real-world applications: 1) only limited labels are available for model training, due to expensive annotation costs over medical images; 2) labeled images may contain considerable label noise (e.g., mislabeling labels) due to diagnostic difficulties of diseases. To address these, we seek to exploit rich labeled data from relevant domains to help the learning in the target task via {Unsupervised Domain Adaptation} (UDA). Unlike most UDA methods that rely on clean labeled data or assume samples are equally transferable, we innovatively propose a Collaborative Unsupervised Domain Adaptation algorithm, which conducts transferability-aware adaptation and conquers label noise in a collaborative way. We theoretically analyze the generalization performance of the proposed method, and also empirically evaluate it on both medical and general images. Promising experimental results demonstrate the superiority and generalization of the proposed method.

中文翻译：

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用于医学图像诊断的协同无监督域适应

基于深度学习的医学图像诊断在临床医学中显示出巨大的潜力。然而，它在实际应用中经常遇到两个主要困难：1）由于医学图像的昂贵注释成本，只有有限的标签可用于模型训练；2）由于疾病的诊断困难，标记图像可能包含相当大的标签噪声（例如，错误标记标签）。为了解决这些问题，我们寻求利用来自相关领域的丰富标记数据，通过{无监督领域适应}（UDA）帮助目标任务中的学习。与大多数依赖于干净标记数据或假设样本具有同等可迁移性的 UDA 方法不同，我们创新地提出了一种协作无监督域适应算法，该算法进行可迁移性感知适应并以协作方式克服标签噪声。我们从理论上分析了所提出方法的泛化性能，并在医学和一般图像上对其进行了实证评估。有希望的实验结果证明了所提出方法的优越性和通用性。