RLAD: A reliable hippo-guided multi-task model for Alzheimer's disease diagnosis
Document Type
Journal Article
Publication Title
IEEE Journal of Biomedical and Health Informatics
PubMed ID
38861438
Publisher
IEEE
School
School of Science
Abstract
Early diagnosis of Alzheimer's disease (AD) is crucial for its prevention, and hippocampal atrophy is a significant lesion for early diagnosis. The current DL-based AD diagnosis methods only focus on either AD classification or hippocampus segmentation independently, neglecting the correlation between the two tasks and lacking pathological interpretability. To address this issue, we propose a Reliable Hippo-guided Learning model for Alzheimer's Disease diagnosis (RLAD), which employs multi-task learning for AD classification as a main task supplemented by hippocampus segmentation. More specifically, our model consists of 1) a hybrid shared features encoder that encodes local and global information in MRI to enhance the model's ability to learn discriminative features; 2) Task Specific Decoders to accomplish AD classification and hippocampus segmentation; and 3) Task Coordination module to correlate the two tasks and guide the classification task to focus on the hippocampus area. Our proposed RLAD model is evaluated on MRI scans of 1631 subjects from three independent datasets, including ADNI-1, ADNI-2, and HarP. Our extensive experimental results demonstrate that the proposed model significantly improves the performance of AD classification and hippocampus segmentation with strong generalization capabilities. Our implementation and model are available at https://github.com/LeoLjl/Explainable-Alzheimer-s-Disease-Diagnosis.
DOI
10.1109/JBHI.2024.3412926
Access Rights
subscription content
Comments
Lei, Z., Zhu, W., Liu, J., Hua, C., Li, J., Shah, S. A. A., ... & Mao, C. (2024). RLAD: A Reliable Hippo-guided Multi-task Model for Alzheimer's Disease Diagnosis. IEEE Journal of Biomedical and Health Informatics. https://doi.org/10.1109/JBHI.2024.3412926