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

# metadata.dc.description.abstract[].value
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.

# metadata.dc.relation.isformatof[].value
Versió postprint de la comunicació Mitigating Overdiagnosis Bias in CNN-Based
Alzheimer’s Disease Diagnosis for the Elderly del congrés publicat a https://doi.org/10.1007/978-3-031-72787-0_5

# metadata.dc.relation.ispartof[].value
Comunicació a: Ethics and Fairness in Medical Imaging: Second International Workshop on Fairness of AI in Medical Imaging, FAIMI 2024, and Third International Workshop on Ethical and Philosophical Issues in Medical Imaging, EPIMI 2024, Held in Conjunction with MICCAI 2024, Marrakesh, Morocco, October 6–10, 2024

# metadata.dc.rights[].value
Springer Nature Switzerland AG (c) Vien Ngoc Dang, et al., 2025

# metadata.dc.source[].value
Comunicacions a congressos  (Matemàtiques i Informàtica)

# metadata.dc.title[].value
Mitigating Overdiagnosis Bias in CNN-Based Alzheimer’s Disease Diagnosis for the Elderly