Publications by authors named "Min Gu Kwak"
Self-Supervised Contrastive Learning to Predict the Progression of Alzheimer's Disease with 3D Amyloid-PET.
Bioengineering (Basel)
September 2023
Early diagnosis of Alzheimer's disease (AD) is an important task that facilitates the development of treatment and prevention strategies, and may potentially improve patient outcomes. Neuroimaging has shown great promise, including the amyloid-PET, which measures the accumulation of amyloid plaques in the brain-a hallmark of AD. It is desirable to train end-to-end deep learning models to predict the progression of AD for individuals at early stages based on 3D amyloid-PET.
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- Early detection of Alzheimer's Disease is vital yet challenging due to incomplete patient imaging data, which is often caused by factors like cost and access to technology.
- The proposed deep learning framework uses Mutual Knowledge Distillation (MKD) to effectively model different patient sub-groups based on available imaging modalities, allowing for better diagnosis.
- The framework's effectiveness is demonstrated through simulations and a case study using Alzheimer's Disease Neuroimaging Initiative (ADNI) datasets, showcasing its potential to enhance early diagnosis despite data limitations.*
Early diagnosis of Alzheimer's disease (AD) is an important task that facilitates the development of treatment and prevention strategies and may potentially improve patient outcomes. Neuroimaging has shown great promise, including the amyloid-PET which measures the accumulation of amyloid plaques in the brain - a hallmark of AD. It is desirable to train end-to-end deep learning models to predict the progression of AD for individuals at early stages based on 3D amyloid-PET.
View Article and Find Full Text PDF