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

  • 会议论文

  • 发表日期：

    2025年

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  • Vien Ngoc Dang,
  • Adrià Casamitjana,
  • Jerónimo Hernández-González,
  • Karim Lekadir,
  • for the Alzheimer’s Disease Neuroimaging Initiative

摘要

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 .