Spatial and depth weighted neural network for diagnosis of Alzheimer’s disease

Qingfeng Li; Quan Huo; Xiaodan Xing; Yiqiang Zhan; Xiang Sean Zhou; Feng Shi

doi:10.1117/12.2512645

13 March 2019 Spatial and depth weighted neural network for diagnosis of Alzheimer’s disease

Qingfeng Li, Quan Huo, Xiaodan Xing, Yiqiang Zhan, Xiang Sean Zhou, Feng Shi

Proceedings Volume 10950, Medical Imaging 2019: Computer-Aided Diagnosis; 1095028 (2019) https://doi.org/10.1117/12.2512645
Event: SPIE Medical Imaging, 2019, San Diego, California, United States

Abstract

Objective and efficient diagnosis of Alzheimer’s disease (AD) has been a major topic with extensive researches in recent years, and some promising results have been shown for imaging markers using magnetic resonance imaging (MRI) data. Beside conventional machine learning methods, deep learning based methods have been developed in several studies, where layer-by-layer neural network settings were purposed to extract features for disease classification from the patches or whole images. However, as the disease develops from subcortical nuclei to cortical regions, specific brain regions with morphological changes might contribute to the diagnosis of disease progress. Therefore, we propose a novel spatial and depth weighted neural network structure to extract effective features, and further improve the performance of AD diagnosis. Specifically, we first use group comparison to detect the most distinctive AD-related landmarks, and then sample landmark-based image patches as our training data. In the model structure, with a 15-layer DenseNet as backbone, we introduce a attention bypass to estimate the spatial weights in the image space to guide the network to focus on specific regions. A squeeze-and-excitation (SE) mechanism is also adopted to further weight the feature map channels. We used 2335 subjects from public datasets (i.e., ADNI-1, ADNI-2 and ADNI-GO) for experiment and results show that our framework achieves 90.02% accuracy, 81.25% sensitivity, and 96.33% specificity in diagnosis AD patients from normal controls.

Citation Download Citation

Qingfeng Li, Quan Huo, Xiaodan Xing, Yiqiang Zhan, Xiang Sean Zhou, and Feng Shi "Spatial and depth weighted neural network for diagnosis of Alzheimer’s disease", Proc. SPIE 10950, Medical Imaging 2019: Computer-Aided Diagnosis, 1095028 (13 March 2019); https://doi.org/10.1117/12.2512645

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available