Interpretability of deep neural networks used for the diagnosis of Alzheimer's disease

Abstract

Alzheimer's disease (AD) is a chronic brain disorder and is the most common cause of dementia. Patients suffering from AD experience memory loss, confusion, and other cognitive and behavioral complications. As the disease progresses, these symptoms become severe enough to interfere with the patient's daily life. Since AD is an irreversible disease and existing treatments can only slow down its progress, early diagnosis of AD is a key moment in fighting this disease. In this article, we propose a novel approach for diagnosing AD via deep neural networks from magnetic resonance imaging images. Additionally, we propose three new propagation rules for the layer‐wise relevance propagation (LRP) method, which is a method used for visualizing evidence in deep neural networks to obtain a better understanding of the network's behavior. We also propose various rule configurations for the LRP to achieve better interpretability of the network. Our proposed classification method achieves a 92% accuracy when classifying AD versus healthy controls, which is comparable to state‐of‐the‐art approaches and could potentially aid doctors in AD diagnosis and reduce the occurrence of human error. Our proposed visualization approaches also show improvements in evidence visualization, which helps the spread of computer‐aided diagnosis in the medical domain by eliminating the “black‐box” nature of the neural networks.

Document Details

Document Type
Pub Defense Publication
Publication Date
Sep 28, 2021
Source ID
10.1002/ima.22657

Entities

People

  • Marek Jakab
  • Tomáš Pohl
  • Wanda Benešová

Organizations

  • AbbVie
  • Alzheimer's Drug Discovery Foundation
  • Biogen
  • Canadian Institutes of Health Research
  • Chiron Corporation
  • GE HealthCare
  • Hoffmann-La Roche
  • Laboratoires Servier
  • National Institute of Biomedical Imaging and Bioengineering
  • National Institute on Aging
  • National Institutes of Health
  • Pfizer
  • Roche (United States)
  • Takeda Pharmaceutical Company
  • United States Department of Defense

Tags

Fields of Study

  • Computer science

Readers

  • Computer Networking
  • Gulf War Illness and Chronic Multisymptom Illness in Veterans.
  • Neural Network Machine Learning.

Technology Areas

  • AI & ML
  • AI & ML - Neural Networks