Skin Deep Unlearning: Artefact and Instrument Debiasing in the Context of Melanoma Classification

TL;DR

This paper addresses biases in melanoma classification CNNs by applying bias unlearning techniques to mitigate artefacts like surgical markings and imaging instruments, improving model robustness.

Contribution

It introduces the application of two bias unlearning methods to reduce artefact and instrument biases in melanoma classification, demonstrating their effectiveness.

Findings

Biases from surgical markings are mitigated.

Instrument-related biases are reduced.

Different unlearning methods excel at different bias mitigation tasks.

Abstract

Convolutional Neural Networks have demonstrated dermatologist-level performance in the classification of melanoma from skin lesion images, but prediction irregularities due to biases seen within the training data are an issue that should be addressed before widespread deployment is possible. In this work, we robustly remove bias and spurious variation from an automated melanoma classification pipeline using two leading bias unlearning techniques. We show that the biases introduced by surgical markings and rulers presented in previous studies can be reasonably mitigated using these bias removal methods. We also demonstrate the generalisation benefits of unlearning spurious variation relating to the imaging instrument used to capture lesion images. Our experimental results provide evidence that the effects of each of the aforementioned biases are notably reduced, with different debiasing techniques excelling at different tasks.