2021 IEEE International Conference on Image Processing

19-22 September 2021 • Anchorage, Alaska, USA

Imaging Without Borders

2021 IEEE International Conference on Image Processing

19-22 September 2021 • Anchorage, Alaska, USA

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Paper Detail

Paper IDMLR-APPL-BSIP.11
Paper Title Enhancing Alzheimer's Disease Diagnosis via Hierarchical 3D-FCN with Multi-Modal Features
Authors Chao Liu, Xiaodong Yang, Dading Chong, Peking University, China; Wenwu Wang, University of Surrey, United Kingdom; Liang Li, Peking University, China
SessionMLR-APPL-BSIP: Machine learning for biomedical signal and image processing
LocationArea C
Session Time:Wednesday, 22 September, 08:00 - 09:30
Presentation Time:Wednesday, 22 September, 08:00 - 09:30
Presentation Poster
Topic Applications of Machine Learning: Machine learning for biomedical signal and image processing
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Abstract Alzheimer’s disease (AD) is an incurable, progressive neurological disorder of the human brain related to loss of memory, commonly seen in the elderly population. Accurate detection of AD can help with proper treatment and prevent brain function damage. Existing CNN-based methods need to predetermine informative locations in sMRI, which means the stage of distinguishing lesions is separated from the later stages of feature extraction and classifier construction. In this paper, a novel ``two-stage" framework based on a hierarchical 3D fully convolutional network (H-3D-FCN) is proposed to automatically identify discriminative local patches and regions in the sMRI. We further optimize the diagnosis performance by constructing a multi-layer perceptron (MLP) model which combines the multi-modal features (e.g., MMSE score, age, gender, APOE 4) with the risk probability maps (RPMs) generated from the H-3D-FCN model. Experiments on three typical AD datasets, namely, ADNI, AIBL, and NACC, show that our method achieves state-of-the-art performance as compared with recent baselines.