Prediction of Progression to Alzheimer's disease with Deep InfoMax
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Abstract (arXiv)
Arguably, unsupervised learning plays a crucial role in the majority of algorithms for processing brain imaging. A recently introduced unsupervised approach Deep InfoMax (DIM) is a promising tool for exploring brain structure in a flexible non-linear way. In this paper, we investigate the use of variants of DIM in a setting of progression to Alzheimer's disease in comparison with supervised AlexNet and ResNet inspired convolutional neural networks. As a benchmark, we use a classification task between four groups: patients with stable, and progressive mild cognitive impairment (MCI), with Alzheimer's disease, and healthy controls. Our dataset is comprised of 828 subjects from the Alzheimer's Disease Neuroimaging Initiative (ADNI) database. Our experiments highlight encouraging evidence of the high potential utility of DIM in future neuroimaging studies.
Key facts. Cited by 3 papers in the map (data through 14 Oct 2026). First posted on arXiv 24 Apr 2019.
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Citing papers per year: 2021: 2; 2022: 1.
3 papers.
- 2021Tasting the cake: evaluating self-supervised generalization on out-of-distribution multimodal MRI datacited by 5
- 2021Detection of Alzheimer's Disease Using Graph-Regularized Convolutional Neural Network Based on Structural Similarity Learning of Brain Magnetic Resonance Imagescited by 1
- 2022Self-supervised multimodal neuroimaging yields predictive representations for a spectrum of Alzheimer's phenotypescited by 1
- 2022Self-supervised multimodal neuroimaging yields predictive representations for a spectrum of Alzheimer's phenotypes7 Sep 2022
- 2021Tasting the cake: evaluating self-supervised generalization on out-of-distribution multimodal MRI data29 Mar 2021
- 2021Detection of Alzheimer's Disease Using Graph-Regularized Convolutional Neural Network Based on Structural Similarity Learning of Brain Magnetic Resonance Images25 Feb 2021