  Recent years have witnessed a rapid development of automated methods for skin
lesion diagnosis and classification. Due to an increasing deployment of such
systems in clinics, it has become important to develop a more robust system
towards various Out-of-Distribution(OOD) samples (unknown skin lesions and
conditions). However, the current deep learning models trained for skin lesion
classification tend to classify these OOD samples incorrectly into one of their
learned skin lesion categories. To address this issue, we propose a simple yet
strategic approach that improves the OOD detection performance while
maintaining the multi-class classification accuracy for the known categories of
skin lesion. To specify, this approach is built upon a realistic scenario of a
long-tailed and fine-grained OOD detection task for skin lesion images. Through
this approach, 1) First, we target the mixup amongst middle and tail classes to
address the long-tail problem. 2) Later, we combine the above mixup strategy
with prototype learning to address the fine-grained nature of the dataset. The
unique contribution of this paper is two-fold, justified by extensive
experiments. First, we present a realistic problem setting of OOD task for skin
lesion. Second, we propose an approach to target the long-tailed and
fine-grained aspects of the problem setting simultaneously to increase the OOD
performance.
