Differential Diagnosis of Frontotemporal Dementia and Alzheimer's Disease using Generative Adversarial Network
This addresses the challenge of misdiagnosis between two common dementia types for medical practitioners, though it appears incremental as it applies existing deep learning techniques to a new medical imaging task.
The study tackled the problem of differentiating between Frontotemporal Dementia and Alzheimer's Disease using MRI scans, achieving high accuracy in experiments with 1,954 images through a novel Generative Adversarial Network framework.
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.