A survey on active learning and human-in-the-loop deep learning for medical image analysis
Fully automatic deep learning has become the state-of-the-art technique for many tasks
including image acquisition, analysis and interpretation, and for the extraction of clinically …
including image acquisition, analysis and interpretation, and for the extraction of clinically …
[HTML][HTML] The role of generative adversarial networks in brain MRI: a scoping review
The performance of artificial intelligence (AI) for brain MRI can improve if enough data are
made available. Generative adversarial networks (GANs) showed a lot of potential to …
made available. Generative adversarial networks (GANs) showed a lot of potential to …
BPGAN: Brain PET synthesis from MRI using generative adversarial network for multi-modal Alzheimer's disease diagnosis
Abstract Background and Objective Multi-modal medical images, such as magnetic
resonance imaging (MRI) and positron emission tomography (PET), have been widely used …
resonance imaging (MRI) and positron emission tomography (PET), have been widely used …
Task-induced pyramid and attention GAN for multimodal brain image imputation and classification in Alzheimer's disease
With the advance of medical imaging technologies, multimodal images such as magnetic
resonance images (MRI) and positron emission tomography (PET) can capture subtle …
resonance images (MRI) and positron emission tomography (PET) can capture subtle …
[HTML][HTML] Bidirectional mapping of brain MRI and PET with 3D reversible GAN for the diagnosis of Alzheimer's disease
Combining multi-modality data for brain disease diagnosis such as Alzheimer's disease
(AD) commonly leads to improved performance than those using a single modality …
(AD) commonly leads to improved performance than those using a single modality …
Brain tumor segmentation based on the dual-path network of multi-modal MRI images
L Fang, X Wang - Pattern Recognition, 2022 - Elsevier
Because of the tumor with infiltrative growth, the glioma boundary is usually fused with the
brain tissue, which leads to the failure of accurately segmenting the brain tumor structure …
brain tissue, which leads to the failure of accurately segmenting the brain tumor structure …
[HTML][HTML] Cascaded multi-modal mixing transformers for alzheimer's disease classification with incomplete data
Accurate medical classification requires a large number of multi-modal data, and in many
cases, different feature types. Previous studies have shown promising results when using …
cases, different feature types. Previous studies have shown promising results when using …
[HTML][HTML] Applications of generative adversarial networks in neuroimaging and clinical neuroscience
Generative adversarial networks (GANs) are one powerful type of deep learning models that
have been successfully utilized in numerous fields. They belong to the broader family of …
have been successfully utilized in numerous fields. They belong to the broader family of …
Handling missing values in healthcare data: A systematic review of deep learning-based imputation techniques
Objective The proper handling of missing values is critical to delivering reliable estimates
and decisions, especially in high-stakes fields such as clinical research. In response to the …
and decisions, especially in high-stakes fields such as clinical research. In response to the …
Iterative sparse and deep learning for accurate diagnosis of Alzheimer's disease
Deep learning techniques have been increasingly applied to the diagnosis of Alzheimer's
disease (AD) and the conversion from mild cognitive impairment (MCI) to AD. Despite their …
disease (AD) and the conversion from mild cognitive impairment (MCI) to AD. Despite their …