Prediction of Progression to Alzheimer's disease with Deep InfoMax
This work addresses Alzheimer's disease prediction for medical researchers, but it is incremental as it applies an existing unsupervised method to a new dataset.
The paper tackled predicting progression to Alzheimer's disease using Deep InfoMax (DIM) variants, comparing them to supervised CNNs on a dataset of 828 subjects, finding DIM shows high potential utility for neuroimaging studies.
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.