Addressing the Real-world Class Imbalance Problem in Dermatology
This addresses a practical issue for medical diagnosis in dermatology, but it is incremental as it builds on existing methods.
The paper tackled the class imbalance problem in dermatology by comparing few-shot learning and conventional techniques, finding that an ensemble of both approaches improved performance for rare classes.
Class imbalance is a common problem in medical diagnosis, causing a standard classifier to be biased towards the common classes and perform poorly on the rare classes. This is especially true for dermatology, a specialty with thousands of skin conditions but many of which have low prevalence in the real world. Motivated by recent advances, we explore few-shot learning methods as well as conventional class imbalance techniques for the skin condition recognition problem and propose an evaluation setup to fairly assess the real-world utility of such approaches. We find the performance of few-show learning methods does not reach that of conventional class imbalance techniques, but combining the two approaches using a novel ensemble improves model performance, especially for rare classes. We conclude that ensembling can be useful to address the class imbalance problem, yet progress can further be accelerated by real-world evaluation setups for benchmarking new methods.