Towards Reliable Dermatology Evaluation Benchmarks
Proceedings of the 3rd Machine Learning for Health Symposium, PMLR 225:101-128, 2023.
Benchmark datasets for digital dermatology unwittingly contain inaccuracies that reduce trust in model performance estimates. We propose a resource-efficient data-cleaning protocol to identify issues that escaped previous curation. The protocol leverages an existing algorithmic cleaning strategy and is followed by a confirmation process terminated by an intuitive stopping criterion. Based on confirmation by multiple dermatologists, we remove irrelevant samples and near duplicates and estimate the percentage of label errors in six dermatology image datasets for model evaluation promoted by the isic . Along with this paper, we publish revised file lists for each dataset which should be used for model evaluation. https://github.com/Digital-Dermatology/SelfClean-Revised-Benchmarks Our work paves the way for more trustworthy performance assessment in digital dermatology.