Fairness Evolution in Continual Learning for Medical Imaging

Marina Ceccon; Davide Dalle Pezze; Alessandro Fabris; Gian Antonio Susto

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arXiv · 2406.02480

Fairness Evolution in Continual Learning for Medical Imaging

Marina Ceccon·Davide Dalle Pezze·Alessandro Fabris·Gian Antonio Susto

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Abstract

Deep Learning has advanced significantly in medical applications, aiding disease diagnosis in Chest X-ray images. However, expanding model capabilities with new data remains a challenge, which Continual Learning (CL) aims to address. Previous studies have evaluated CL strategies based on classification performance; however, in sensitive domains such as healthcare, it is crucial to assess performance across socially salient groups to detect potential biases. This study examines how bias evolves across tasks using domain-specific fairness metrics and how different CL strategies impact this evolution. Our results show that Learning without Forgetting and Pseudo-Label achieve optimal classification performance, but Pseudo-Label is less biased.

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Marina Ceccon, Davide Dalle Pezze, Alessandro Fabris, Gian Antonio Susto. 2024-04-10. Fairness Evolution in Continual Learning for Medical Imaging. https://arxiv.org/abs/2406.02480

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