The development of reliable and fair diagnostic systems is often constrained by the scarcity of labeled data. To address this challenge, our work explores the feasibility of unsupervised domain adaptation (UDA) to integrate large external datasets for developing reliable classifiers. The adoption of UDA with multiple sources can simultaneously enrich the training set and bridge the domain gap between different skin lesion datasets, which vary due to distinct acquisition protocols. Particularly, UDA shows practical promise for improving diagnostic reliability when training with a custom skin lesion dataset, where only limited labeled data are available from the target domain. In this study, we investigate three UDA training schemes based on source data utilization: single-source, combined-source, and multi-source UDA. Our findings demonstrate the effectiveness of applying UDA on multiple sources for binary and multi-class classification. A strong correlation between test error and label shift in multi-class tasks has been observed in the experiment. Crucially, our study shows that UDA can effectively mitigate bias against minority groups and enhance fairness in diagnostic systems, while maintaining superior classification performance. This is achieved even without directly implementing fairness-focused techniques. This success is potentially attributed to the increased and well-adapted demographic information obtained from multiple sources.

翻译：可靠且公平的诊断系统的开发常常受到标注数据稀缺的限制。为应对这一挑战，本研究探索了利用无监督域适应（UDA）整合大规模外部数据集以开发可靠分类器的可行性。采用多源UDA可同时丰富训练集，并弥合因不同采集协议而存在域差异的各类皮肤病变数据集之间的域鸿沟。值得注意的是，当使用仅含有目标域有限标注数据的定制皮肤病变数据集进行训练时，UDA在提升诊断可靠性方面展现出实际应用前景。本研究基于源数据利用方式，探究了三种UDA训练方案：单源UDA、组合源UDA和多源UDA。实验结果表明，在多源数据上应用UDA对二分类和多分类任务均具有显著效果。实验观察到多分类任务中测试误差与标签偏移之间存在强相关性。关键的是，本研究表明UDA能够有效缓解对少数群体的偏见，增强诊断系统的公平性，同时保持优越的分类性能。这一成果甚至无需直接采用聚焦公平性的技术即可实现，其成功可能归因于从多个数据源获取了更丰富且适应良好的群体信息。