  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.
