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Mitigating Overdiagnosis Bias.pdf (302.84 KB)

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Accepted version

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2024-10-13

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Please use this identifier to cite or link to this item: https://hdl.handle.net/2445/220330

Mitigating Overdiagnosis Bias in CNN-Based Alzheimer’s Disease Diagnosis for the Elderly

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Abstract

Diagnosing Alzheimer’s disease (AD) presents significant challenges in the oldest populations due to overlapping symptoms of normal cognitive aging and early-stage dementia. While AI algorithms have matched specialist performance in diagnosing AD, they tend to produce unreliable results for the oldest populations, generating false positives that increase radiologist workloads and healthcare costs. In this study, we focus on mitigating overdiagnosis bias in CNN-based AD diagnosis for these groups. We present a post-hoc bias mitigation technique that significantly improves fairness by reducing overdiagnosis and enhances reliability by improving calibration without compromising overall model accuracy. Code is available at: https://github.com/ngoc-vien-dang/C-GTOP.

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Xarxes neuronals convolucionals, Malaltia d'Alzheimer, Imatges mèdiques

Subject (English)

Convolutional neural networks, Alzheimer's disease, Imaging systems in medicine

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NGOC DANG, Vien, et al. Mitigating Overdiagnosis Bias in CNN-Based Alzheimer’s Disease Diagnosis for the Elderly. Comunicació a: Ethics and Fairness in Medical Imaging: Second International Workshop on Fairness of AI in Medical Imaging. FAIMI 2024. Vol.  and Third International Workshop on Ethical and Philosophical Issues in Medical Imaging, num. EPIMI 2024, pags. Held in Conjunction with MICCAI 2024. ISBN 978-3-031-72787-0. [consulted: 19 of August of 2026]. Available at: https://hdl.handle.net/2445/220330

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