Disentangle First, Then Distill: A Unified Framework for Missing Modality Imputation and Alzheimer’s Disease Diagnosis-Reference-Cited by-同舟云学术

Disentangle First, Then Distill: A Unified Framework for Missing Modality Imputation and Alzheimer’s Disease Diagnosis

Published:2023-12 Issue:12 Volume:42 Page:3566-3578
ISSN:0278-0062
Container-title:IEEE Transactions on Medical Imaging
language:
Short-container-title:IEEE Trans. Med. Imaging

Author:

Chen Yuanyuan¹^ORCID,Pan Yongsheng²^ORCID,Xia Yong¹^ORCID,Yuan Yixuan³^ORCID

Affiliation:

1. National Engineering Laboratory for Integrated Aero-Space-Ground-Ocean Big Data Application Technology, School of Computer Science and Engineering, Northwestern Polytechnical University, Xi’an, China

2. School of Biomedical Engineering, ShanghaiTech University, Shanghai, China

3. Department of Electronic Engineering, The Chinese University of Hong Kong, Hong Kong, SAR, China

Funder

National Key Research and Development Programof China

China Postdoctoral Science Foundation

Innovation and Technology Commission-Innovation and Technology Fund

Alzheimer’s Disease Neuroimaging Initiative (ADNI)

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

Electrical and Electronic Engineering,Computer Science Applications,Radiological and Ultrasound Technology,Software

Link

http://xplorestaging.ieee.org/ielx7/42/10336247/10184044.pdf?arnumber=10184044

Reference51 articles.

1. MRI-based Alzheimer’s disease prediction via distilling the knowledge in multi-modal data

2. Deep Multi-modal Latent Representation Learning for Automated Dementia Diagnosis

3. Inter-modality Dependence Induced Data Recovery for MCI Conversion Prediction

4. Multimodal Learning with Incomplete Modalities by Knowledge Distillation

5. Latent Representation Learning for Alzheimer’s Disease Diagnosis With Incomplete Multi-Modality Neuroimaging and Genetic Data

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