Achieving Reliable and Fair Skin Lesion Diagnosis via Unsupervised Domain Adaptation

arXiv:2307.03157v2 Announce Type: replace-cross
Abstract: 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 …

abstract adoption arxiv challenge classifiers cs.cv cs.cy cs.lg data datasets development diagnosis diagnostic domain domain adaptation fair systems type unsupervised via work