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

Ngoc Dang, VienCasamitjana, AdriàHernández-González, JerónimoLekadir, Karim, 1977-2025-04-082025-04-082024-10-13978-3-031-72787-0https://hdl.handle.net/2445/220330Diagnosing 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.10 p.application/pdfengSpringer Nature Switzerland AG (c) Vien Ngoc Dang, et al., 2025Xarxes neuronals convolucionalsMalaltia d'AlzheimerImatges mèdiquesConvolutional neural networksAlzheimer's diseaseImaging systems in medicineMitigating Overdiagnosis Bias in CNN-Based Alzheimer’s Disease Diagnosis for the Elderlyinfo:eu-repo/semantics/conferenceObjectinfo:eu-repo/semantics/openAccess