Contrastive self-supervised learning for neurodegenerative disorder classification.

ABSTRACT:

Introduction

Neurodegenerative diseases such as Alzheimer's disease (AD) or frontotemporal lobar degeneration (FTLD) involve specific loss of brain volume, detectable in vivo using T1-weighted MRI scans. Supervised machine learning approaches classifying neurodegenerative diseases require diagnostic-labels for each sample. However, it can be difficult to obtain expert labels for a large amount of data. Self-supervised learning (SSL) offers an alternative for training machine learning models without data-labels.

Methods

We investigated if the SSL models can be applied to distinguish between different neurodegenerative disorders in an interpretable manner. Our method comprises a feature extractor and a downstream classification head. A deep convolutional neural network,

SUBMITTER: Gryshchuk V 

PROVIDER: S-EPMC11873101 | biostudies-literature | 2025

REPOSITORIES: biostudies-literature