Article

Self-Supervised Contrastive Learning to Predict Alzheimer’s Disease Progression with 3D Amyloid-PET

2023-04-26

Abstract excerpt

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...

Topics

Open a Topic to create a Post that cites this publication.

Identifiers and source

Literature Corpus work: e4ba8156-7641-554d-baa3-90d9f8d8ac6f
DOI: 10.1101/2023.04.20.23288886

Open publication

Related research

Semantic proximity does not establish scientific evidence.

Click a neighbor to travelStep 1 · 12 closest

Self-Supervised Contrastive Learning to Predict Alzheimer’s Disease Progression with 3D Amyloid-PETDOI 10.1101/2023.04.20.23288886Read

Contrastive Self-supervised Learning for Neurodegenerative Disorder ClassificationSemantic proximity Read

A Semi-Supervised Contrastive Learning Approach to Alzheimer’s Disease Diagnostics using Convolutional AutoencodersSemantic proximity Read

Weakly Supervised Learning for Alzheimer’s Disease Diagnosis Using MRI and Clinical DataSemantic proximity Read

Three-round learning strategy based on 3D Deep Convolutional GANs for Alzheimer’s disease stagingSemantic proximity Read

Multimodal deep learning framework for Alzheimer’s stage classification using GAN-augmented MRI and clinical dataSemantic proximity Read

Advanced Deep Learning Framework for Early Alzheimer’s Disease Classification and Disease MonitoringSemantic proximity Read

Classification of Alzheimer's Disease with and without Imagery Using Gradient Boosted Machines and ResNet-50Semantic proximity Read

WITHDRAWN: A Hybrid Deep Learning Framework to Predict Alzheimer’s Disease Progression Using Generative Adversarial Networks and Deep Convolutional Neural NetworksSemantic proximity Read

Learning a Continuous Progression Trajectory of Amyloid in Alzheimer diseaseSemantic proximity Read

A Practical Alzheimer Disease Classifier via Brain Imaging-Based Deep Learning on 85,721 SamplesSemantic proximity Read

Using Deep Learning to Determine Amyloid Deposition through PET and Clinical Data for Alzheimer’s PrognosisSemantic proximity Read

A Practical Alzheimer Disease Classifier via Brain Imaging-Based Deep Learning on 85,721 SamplesSemantic proximity Read