Differential Diagnosis of Frontotemporal Dementia and Alzheimer's Disease using Generative Adversarial Network

TL;DR

This paper introduces a novel GAN-based framework that leverages multi-scale structural features and synthetic data augmentation to accurately differentiate between frontotemporal dementia, Alzheimer's disease, and normal controls using MRI scans.

Contribution

It is the first to apply a GAN-based approach for neuroimage-based differential diagnosis of dementia sub-types, improving classification accuracy.

Findings

Achieved high accuracy with 10-fold cross-validation on 1,954 MRI images.

Demonstrated that multi-scale features and synthetic data enhance differentiation performance.

Showed the effectiveness of GAN-generated data in challenging neurodiagnostic tasks.

Abstract

Frontotemporal dementia and Alzheimer's disease are two common forms of dementia and are easily misdiagnosed as each other due to their similar pattern of clinical symptoms. Differentiating between the two dementia types is crucial for determining disease-specific intervention and treatment. Recent development of Deep-learning-based approaches in the field of medical image computing are delivering some of the best performance for many binary classification tasks, although its application in differential diagnosis, such as neuroimage-based differentiation for multiple types of dementia, has not been explored. In this study, a novel framework was proposed by using the Generative Adversarial Network technique to distinguish FTD, AD and normal control subjects, using volumetric features extracted at coarse-to-fine structural scales from Magnetic Resonance Imaging scans. Experiments of 10-folds cross-validation on 1,954 images achieved high accuracy. With the proposed framework, we have demonstrated that the combination of multi-scale structural features and synthetic data augmentation based on generative adversarial network can improve the performance of challenging tasks such as differentiating Dementia sub-types.