# Addressing the Real-world Class Imbalance Problem in Dermatology

arXiv: https://arxiv.org/abs/2010.04308 · HTML: https://mlsanity.com/paper/2010.04308/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 9 Oct 2020.

## Built on by (most cited first, 5 of 5)

- [Does Your Dermatology Classifier Know What It Doesn't Know? Detecting the Long-Tail of Unseen Conditions](https://mlsanity.com/paper/2104.03829/) (2021)
- [Bayesian-guided Label Mapping for Visual Reprogramming](https://mlsanity.com/paper/2410.24018/) (2024)
- [Deeply Supervised Skin Lesions Diagnosis with Stage and Branch Attention](https://mlsanity.com/paper/2205.04326/) (2022)
- [Few-shot learning for COVID-19 Chest X-Ray Classification with Imbalanced Data: An Inter vs. Intra Domain Study](https://mlsanity.com/paper/2401.10129/) (2024)
- [Empirical Analysis of Privacy-Fairness-Accuracy Trade-offs in Federated Learning: A Step Towards Responsible AI](https://mlsanity.com/paper/2503.16233/) (2025)

## Abstract (arXiv)

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.