Mitigating the Influence of Domain Shift in Skin Lesion Classification:
  A Benchmark Study of Unsupervised Domain Adaptation Methods on Dermoscopic
  Images
Sireesha Chamarthi Katharina Fogelberg Roman C. Maron Titus J. Brinker Julia Niebling
The potential of deep neural networks in skin lesion classification has
already been demonstrated to be on-par if not superior to the dermatologists
diagnosis. However, the performance of these models usually deteriorates when
the test data differs significantly from the training data (i.e. domain shift).
This concerning limitation for models intended to be used in real-world skin
lesion classification tasks poses a risk to patients. For example, different
image acquisition systems or previously unseen anatomical sites on the patient
can suffice to cause such domain shifts. Mitigating the negative effect of such
shifts is therefore crucial, but developing effective methods to address domain
shift has proven to be challenging. In this study, we carry out an in-depth
analysis of eight different unsupervised domain adaptation methods to analyze
their effectiveness in improving generalization for dermoscopic datasets. To
ensure robustness of our findings, we test each method on a total of ten
distinct datasets, thereby covering a variety of possible domain shifts. In
addition, we investigated which factors in the domain shifted datasets have an
impact on the effectiveness of domain adaptation methods. Our findings show
that all of the eight domain adaptation methods result in improved AUPRC for
the majority of analyzed datasets. Altogether, these results indicate that
unsupervised domain adaptations generally lead to performance improvements for
the binary melanoma-nevus classification task regardless of the nature of the
domain shift. However, small or heavily imbalanced datasets lead to a reduced
conformity of the results due to the influence of these factors on the methods
performance.