Fairness Evolution in Continual Learning for Medical Imaging
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Abstract (arXiv)
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.
Key facts. Cited by 2 papers in the map (data through 7 Oct 2026). Builds on 36 of its 52 references that are in the map. First posted on arXiv 10 Apr 2024.
Builds on
36 of 52 references.
References · 36
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24 more
- 2017Incremental Learning of Object Detectors without Catastrophic Forgettingcited by 208
- 2020Modeling the Background for Incremental Learning in Semantic Segmentationcited by 189
- 2020CheXclusion: Fairness gaps in deep chest X-ray classifierscited by 153
- 2019Fairness in Deep Learning: A Computational Perspectivecited by 123
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- 2020Imbalanced Continual Learning with Partitioning Reservoir Samplingcited by 71
- 2022MEDFAIR: Benchmarking Fairness for Medical Imagingcited by 66
- 2022Improving the Fairness of Chest X-ray Classifierscited by 52
- 2022Detecting Shortcut Learning for Fair Medical AI using Shortcut Testingcited by 52
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- 2022LifeLonger: A Benchmark for Continual Disease Classificationcited by 28
- 2020What is Wrong with Continual Learning in Medical Image Segmentation?cited by 25
- 2023A Comprehensive Empirical Evaluation on Online Continual Learningcited by 24
- 2020ADER: Adaptively Distilled Exemplar Replay Towards Continual Learning for Session-based Recommendationcited by 22
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- 2023Continual Segment: Towards a Single, Unified and Accessible Continual Segmentation Model of 143 Whole-body Organs in CT Scanscited by 21
- 2021Domain-Incremental Continual Learning for Mitigating Bias in Facial Expression and Action Unit Recognitioncited by 19
- 2022Continual Learning for Class- and Domain-Incremental Semantic Segmentationcited by 17
- 2023Are Sex-based Physiological Differences the Cause of Gender Bias for Chest X-ray Diagnosis?cited by 13
- 2023FALCON: Fairness Learning via Contrastive Attention Approach to Continual Semantic Scene Understandingcited by 13
- 2022Sustaining Fairness via Incremental Learningcited by 5
- 2022A Multi-label Continual Learning Framework to Scale Deep Learning Approaches for Packaging Equipment Monitoringcited by 4
- 2021Towards Fair Affective Robotics: Continual Learning for Mitigating Bias in Facial Expression and Action Unit Recognitioncited by 3
- 2023Class-Incremental Continual Learning for General Purpose Healthcare Modelscited by 3
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Citing papers per year: 2024: 1; 2025: 0; 2026: 1.
2 papers.