Abstract
Medical imaging approaches widely employ deep neural networks for the investigation and diagnosis of different skin disorders. However, recent studies suggest that even a proficient model based on deep learning might struggle with generalization when applied to datasets from disparate cohorts due to domain shift phenomena. Meanwhile, there are usually need many well-labelled images utilized for the training process to attain a stronger level of performance. In order to alleviate the domain shift and the necessity for adequate training data, we introduce a novel method termed as adversarial selective domain adaption with feature cluster (ASDA). It achieves effective performance improvement of model when the target dataset is smaller than the source dataset. Specifically, we generate a set of feature clusters for each sample in the target domain to alleviate the demand for data. Subsequently, a conditional domain adversarial network is used to mitigate domain shift. Finally, due to consistency issues between feature clusters and samples, we propose a method of selective minmax entropy to maintain consistency. Our method diverges from typical domain adaption approaches that solely target reducing the domain gap. Instead, we address both the discrepancy between domains and the problem of limited data in the target dataset simultaneously. Extensive experiments have been undertaken on datasets pertaining to skin cancer, that confirms ASDA's efficacy in skin cancer diagnosis for dermatoscopic and clinic image.