Mr. Qingfeng Li Profile

Qingfeng Li

at Southern Medical Univ

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Proceedings Article | 13 March 2019 Paper

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, 1095028 (2019) https://doi.org/10.1117/12.2512645

KEYWORDS: Feature extraction, Neural networks, Machine learning, Alzheimer's disease, Convolution, Brain, Magnetic resonance imaging, Neuroimaging, Performance modeling, Image classification

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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.

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