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Proceedings Paper

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

Paper Details

Date Published: 13 March 2019
PDF: 8 pages
Proc. SPIE 10950, Medical Imaging 2019: Computer-Aided Diagnosis, 1095028 (13 March 2019); doi: 10.1117/12.2512645
Show Author Affiliations
Qingfeng Li, Southern Medical Univ. (China)
Shanghai United Imaging Intelligence Co., Ltd. (China)
Quan Huo, Shanghai United Imaging Intelligence Co., Ltd. (China)
Xiaodan Xing, Shanghai United Imaging Intelligence Co., Ltd. (China)
Shanghai Advanced Research Institute (China)
Yiqiang Zhan, Shanghai United Imaging Intelligence Co., Ltd. (China)
Xiang Sean Zhou, Shanghai United Imaging Intelligence Co., Ltd. (China)
Feng Shi, Shanghai United Imaging Intelligence Co., Ltd. (China)


Published in SPIE Proceedings Vol. 10950:
Medical Imaging 2019: Computer-Aided Diagnosis
Kensaku Mori; Horst K. Hahn, Editor(s)

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