Abstract

Deep neural networks show great promise for classifying brain diseases and making prognostic assessments based on neuroimaging data, but large, labeled training datasets are often required to achieve high predictive accuracy. Here we evaluated a range of transfer learning or pre-training strategies to create useful MRI representations for downstream tasks that lack large amounts of training data, such as Alzheimer’s disease (AD) classification. To test our proposed pretraining strategies, we analyzed 4,098 3D T1-weighted brain MRI scans from the Alzheimer's Disease Neuroimaging Initiative (ADNI) cohort and independently validated with an out-of-distribution test set of 600 scans from the Open Access Series of Imaging Studies (OASIS3) cohort for detecting AD. First, we trained 3D and 2D convolutional neural network (CNN) architectures. We tested combinations of multiple pre-training strategies based on (1 …

Metadata

publication

18th International Symposium on Medical Information Processing and Analysis …, 2023

year

2023

publication date

2023/3/6

authors

Nikhil J Dhinagar, Sophia I Thomopoulos, Priya Rajagopalan, Dimitris Stripelis, Jose Luis Ambite, Greg Ver Steeg, Paul M Thompson

link

https://www.spiedigitallibrary.org/conference-proceedings-of-spie/12567/125671L/Evaluation-of-transfer-learning-methods-for-detecting-Alzheimers-disease-with/10.1117/12.2670457.short

resource_link

https://www.biorxiv.org/content/biorxiv/early/2022/08/25/2022.08.23.505030.full.pdf

conference

18th International Symposium on Medical Information Processing and Analysis

volume

12567

pages

504-513

publisher

SPIE