Abstract

Deep learning models based on convolutional neural networks (CNNs) have been used to classify Alzheimer's disease or infer dementia severity from 3D T1-weighted brain MRI scans. Here, we examine the value of adding occlusion sensitivity analysis (OSA) and gradient-weighted class activation mapping (Grad-CAM) to these models to make the results more interpretable. Much research in this area focuses on specific datasets such as the Alzheimer's Disease Neuroimaging Initiative (ADNI) or National Alzheimer's Coordinating Center (NACC), which assess people of North American, predominantly European ancestry, so we examine how well models trained on these data generalize to a new population dataset from India (NIMHANS cohort). We also evaluate the benefit of using a combined dataset to train the CNN models. Our experiments show feature localization consistent with knowledge of AD from other …

Date

November 13, 2024

Authors

Tamoghna Chattopadhyay, Neha Ann Joshy, Chirag Jagad, Emma J Gleave, Sophia I Thomopoulos, Yixue Feng, Julio E Villalón-Reina, Emily Laltoo, Himanshu Joshi, Ganesan Venkatasubramanian, John P John, Greg Ver Steeg, Jose Luis Ambite, Paul M Thompson

Conference

2024 20th International Symposium on Medical Information Processing and Analysis (SIPAIM)

Pages

1-4

Publisher

IEEE