Interpretable differential diagnosis for Alzheimer's disease and Frontotemporal dementia

TL;DR

This paper introduces an interpretable deep learning framework that combines 3D U-Nets and graph convolutional networks to improve differential diagnosis between Alzheimer's disease and Frontotemporal dementia using brain imaging biomarkers.

Contribution

It presents a novel ensemble approach utilizing deep grading and atrophy features for accurate and interpretable dementia classification, addressing early diagnosis challenges.

Findings

Achieved competitive accuracy in disease detection and differentiation

Provided interpretable brain region maps highlighting abnormal areas

Enhanced classification performance by combining multiple biomarkers

Abstract

Alzheimer's disease and Frontotemporal dementia are two major types of dementia. Their accurate diagnosis and differentiation is crucial for determining specific intervention and treatment. However, differential diagnosis of these two types of dementia remains difficult at the early stage of disease due to similar patterns of clinical symptoms. Therefore, the automatic classification of multiple types of dementia has an important clinical value. So far, this challenge has not been actively explored. Recent development of deep learning in the field of medical image has demonstrated high performance for various classification tasks. In this paper, we propose to take advantage of two types of biomarkers: structure grading and structure atrophy. To this end, we propose first to train a large ensemble of 3D U-Nets to locally discriminate healthy versus dementia anatomical patterns. The result of these models is an interpretable 3D grading map capable of indicating abnormal brain regions. This map can also be exploited in various classification tasks using graph convolutional neural network. Finally, we propose to combine deep grading and atrophy-based classifications to improve dementia type discrimination. The proposed framework showed competitive performance compared to state-of-the-art methods for different tasks of disease detection and differential diagnosis.