Fairness Evolution in Continual Learning for Medical Imaging

• URL: http://arxiv.org/abs/2406.02480v2
• Date: Mon, 07 Jul 2025 11:41:32 GMT
• Title: Fairness Evolution in Continual Learning for Medical Imaging
• Authors: Marina Ceccon, Davide Dalle Pezze, Alessandro Fabris, Gian Antonio Susto,
• Abstract summary: This study examines how bias evolves across tasks using domain-specific fairness metrics and how different CL strategies impact this evolution.<n>Our results show that Learning without Forgetting and Pseudo-Label achieve optimal classification performance, but Pseudo-Label is less biased.
• Score: 47.52603262576663
• License: http://creativecommons.org/licenses/by/4.0/
• 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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