Paper Info
Title
MISS-Net: Multi-view Contrastive Transformer Network for MCI Stages Prediction Using Brain F-18-FDG PET Imaging
Abstract
Mild Cognitive Impairment (MCI) is the transitional stage between healthy aging and dementia. MCI patients are characterized by very subtle changes in the brain. These changes with disease progression might assist the more precise dementia staging, which can reduce the number of Alzheimer's Disease (AD) patients through early intervention. Indeed, subjects diagnosed with MCI could be further divided into sub-categories (stable MCI and progressive MCI) and only part of them will convert to dementia. In this paper, we propose a multi-view contrastive transformer network for MCI sub-categories detection with the aim of early AD conversion prediction. The proposed method is based on a two-stage learning scheme that optimally captures local and global information from F-18 FluoroDeoxyGlucose Positron Emission Tomography (F-18-FDG PET) images. The proposed approach optimally exploits the complementary of the three image projections (axial, sagittal, and coronal), through contrastive learning, for efficient multi-view clinical pattern (embedding) learning. The proposed method has been evaluated on a subset of the ADNI dataset. Obtained results outperform recent uni-modal and multi-modal state-of-the-art approaches in (sMCI) vs. (pMCI) detection. We report an average accuracy, sensitivity, and sensitivity of respectively 87.13%, 90.61%, and 83.65%.
Year
DOI
Venue
2022
10.1007/978-3-031-16919-9_8
PREDICTIVE INTELLIGENCE IN MEDICINE (PRIME 2022)
Keywords
DocType
Volume
Alzheimer's disease, Stable MCI, Progressive MCI, Prediction, Transformer, Contrastive learning, F-18-FDG PET
Conference
13564
ISSN
Citations 
PageRank 
0302-9743
0
0.34
References 
Authors
0
6
Name
Order
Citations
PageRank
Anouar Kherchouche100.34
Olfa Ben-Ahmed200.34
Carole Guillevin300.34
Benoit Tremblais400.34
Christine Fernandez-Maloigne500.34
Remy Guillevin600.34