Graph-based deep learning for medical diagnosis and analysis: past, present and future
With the advances of data-driven machine learning research, a wide variety of prediction
problems have been tackled. It has become critical to explore how machine learning and …
problems have been tackled. It has become critical to explore how machine learning and …
A survey on graph neural networks and graph transformers in computer vision: A task-oriented perspective
Graph Neural Networks (GNNs) have gained momentum in graph representation learning
and boosted the state of the art in a variety of areas, such as data mining (eg, social network …
and boosted the state of the art in a variety of areas, such as data mining (eg, social network …
Analysis of features of Alzheimer's disease: Detection of early stage from functional brain changes in magnetic resonance images using a finetuned ResNet18 network
One of the first signs of Alzheimer's disease (AD) is mild cognitive impairment (MCI), in
which there are small variants of brain changes among the intermediate stages. Although …
which there are small variants of brain changes among the intermediate stages. Although …
Generative AI for brain image computing and brain network computing: a review
Recent years have witnessed a significant advancement in brain imaging techniques that
offer a non-invasive approach to map** the structure and function of the brain …
offer a non-invasive approach to map** the structure and function of the brain …
Morphological feature visualization of Alzheimer's disease via multidirectional perception GAN
The diagnosis of early stages of Alzheimer's disease (AD) is essential for timely treatment to
slow further deterioration. Visualizing the morphological features for early stages of AD is of …
slow further deterioration. Visualizing the morphological features for early stages of AD is of …
Multi-scale enhanced graph convolutional network for mild cognitive impairment detection
As an early stage of Alzheimer's disease (AD), mild cognitive impairment (MCI) is able to be
detected by analyzing the brain connectivity networks. For this reason, we devise a new …
detected by analyzing the brain connectivity networks. For this reason, we devise a new …
Brain stroke lesion segmentation using consistent perception generative adversarial network
The state-of-the-art deep learning methods have demonstrated impressive performance in
segmentation tasks. However, the success of these methods depends on a large amount of …
segmentation tasks. However, the success of these methods depends on a large amount of …
Prior-guided adversarial learning with hypergraph for predicting abnormal connections in Alzheimer's disease
Alzheimer's disease (AD) is characterized by alterations of the brain's structural and
functional connectivity during its progressive degenerative processes. Existing auxiliary …
functional connectivity during its progressive degenerative processes. Existing auxiliary …
Machine learning with multimodal neuroimaging data to classify stages of Alzheimer's disease: A systematic review and meta-analysis
In recent years, Alzheimer's disease (AD) has been a serious threat to human health.
Researchers and clinicians alike encounter a significant obstacle when trying to accurately …
Researchers and clinicians alike encounter a significant obstacle when trying to accurately …
A survey of deep learning for alzheimer's disease
Alzheimer's and related diseases are significant health issues of this era. The
interdisciplinary use of deep learning in this field has shown great promise and gathered …
interdisciplinary use of deep learning in this field has shown great promise and gathered …