Multimodal machine learning in precision health: A scoping review
Abstract Machine learning is frequently being leveraged to tackle problems in the health
sector including utilization for clinical decision-support. Its use has historically been focused …
sector including utilization for clinical decision-support. Its use has historically been focused …
A brief review on multi-task learning
KH Thung, CY Wee - Multimedia Tools and Applications, 2018 - Springer
Abstract Multi-task learning (MTL), which optimizes multiple related learning tasks at the
same time, has been widely used in various applications, including natural language …
same time, has been widely used in various applications, including natural language …
Brain MRI analysis for Alzheimer's disease diagnosis using an ensemble system of deep convolutional neural networks
Alzheimer's disease is an incurable, progressive neurological brain disorder. Earlier
detection of Alzheimer's disease can help with proper treatment and prevent brain tissue …
detection of Alzheimer's disease can help with proper treatment and prevent brain tissue …
Hierarchical feature representation and multimodal fusion with deep learning for AD/MCI diagnosis
For the last decade, it has been shown that neuroimaging can be a potential tool for the
diagnosis of Alzheimer's Disease (AD) and its prodromal stage, Mild Cognitive Impairment …
diagnosis of Alzheimer's Disease (AD) and its prodromal stage, Mild Cognitive Impairment …
Deep learning based imaging data completion for improved brain disease diagnosis
Combining multi-modality brain data for disease diagnosis commonly leads to improved
performance. A challenge in using multi-modality data is that the data are commonly …
performance. A challenge in using multi-modality data is that the data are commonly …
Effective feature learning and fusion of multimodality data using stage‐wise deep neural network for dementia diagnosis
In this article, the authors aim to maximally utilize multimodality neuroimaging and genetic
data for identifying Alzheimer's disease (AD) and its prodromal status, Mild Cognitive …
data for identifying Alzheimer's disease (AD) and its prodromal status, Mild Cognitive …
Deep double incomplete multi-view multi-label learning with incomplete labels and missing views
View missing and label missing are two challenging problems in the applications of multi-
view multi-label classification scenery. In the past years, many efforts have been made to …
view multi-label classification scenery. In the past years, many efforts have been made to …
Distribution-consistent modal recovering for incomplete multimodal learning
Recovering missed modality is popular in incomplete multimodal learning because it usually
benefits downstream tasks. However, the existing methods often directly estimate missed …
benefits downstream tasks. However, the existing methods often directly estimate missed …
Latent feature representation with stacked auto-encoder for AD/MCI diagnosis
Recently, there have been great interests for computer-aided diagnosis of Alzheimer's
disease (AD) and its prodromal stage, mild cognitive impairment (MCI). Unlike the previous …
disease (AD) and its prodromal stage, mild cognitive impairment (MCI). Unlike the previous …
Latent representation learning for Alzheimer's disease diagnosis with incomplete multi-modality neuroimaging and genetic data
The fusion of complementary information contained in multi-modality data [eg, magnetic
resonance imaging (MRI), positron emission tomography (PET), and genetic data] has …
resonance imaging (MRI), positron emission tomography (PET), and genetic data] has …