What is he best known for?
The fields of study he is best known for:
- Artificial intelligence
- Machine learning
- Pattern recognition
Mingxia Liu mainly focuses on Artificial intelligence, Support vector machine, Pattern recognition, Machine learning and Feature selection.
Mingxia Liu works mostly in the field of Artificial intelligence, limiting it down to topics relating to Neuroimaging and, in certain cases, Cognitive impairment, as a part of the same area of interest.
Particularly relevant to Training set is his body of work in Pattern recognition.
Mingxia Liu works mostly in the field of Machine learning, limiting it down to topics relating to Class and, in certain cases, Modality and Sparse approximation.
His biological study spans a wide range of topics, including Artificial neural network, Regularization and Data mining.
His Feature extraction study which covers Feature that intersects with Longitudinal study, Cluster analysis, Representation and Landmark.
His most cited work include:
- Relationship Induced Multi-Template Learning for Diagnosis of Alzheimer’s Disease and Mild Cognitive Impairment (108 citations)
- Domain Transfer Learning for MCI Conversion Prediction (101 citations)
- Landmark-based deep multi-instance learning for brain disease diagnosis (100 citations)
What are the main themes of his work throughout his whole career to date?
His primary scientific interests are in Artificial intelligence, Pattern recognition, Machine learning, Neuroimaging and Discriminative model.
His works in Feature extraction, Feature selection, Deep learning, Feature learning and Feature are all subjects of inquiry into Artificial intelligence.
His Pattern recognition study incorporates themes from Functional magnetic resonance imaging, Cluster analysis and Identification.
His work in the fields of Machine learning, such as Support vector machine and Regularization, overlaps with other areas such as Hypergraph and Modalities.
His Neuroimaging study combines topics from a wide range of disciplines, such as Transfer of learning, Positron emission tomography and Disease, Cognitive impairment.
His Discriminative model research is multidisciplinary, relying on both Manifold, Dementia, Mr images, Convolutional neural network and Data set.
He most often published in these fields:
- Artificial intelligence (93.38%)
- Pattern recognition (63.24%)
- Machine learning (25.74%)
What were the highlights of his more recent work (between 2019-2021)?
- Artificial intelligence (93.38%)
- Pattern recognition (63.24%)
- Deep learning (19.12%)
In recent papers he was focusing on the following fields of study:
The scientist’s investigation covers issues in Artificial intelligence, Pattern recognition, Deep learning, Neuroimaging and Discriminative model.
His Artificial intelligence study incorporates themes from Machine learning and Functional magnetic resonance imaging.
His work on Feature selection and Leverage as part of general Machine learning study is frequently linked to Domain adaptation, therefore connecting diverse disciplines of science.
His Pattern recognition research integrates issues from Disease progression, Mr images and Identification.
Mingxia Liu combines subjects such as Positron emission tomography, Measure and Dementia with his study of Neuroimaging.
His study on Discriminative model also encompasses disciplines like
- Prodromal Stage together with Dementia diagnosis and Landmark,
- Cognitive impairment which intersects with area such as Dependency.
Between 2019 and 2021, his most popular works were:
- Hierarchical Fully Convolutional Network for Joint Atrophy Localization and Alzheimer's Disease Diagnosis Using Structural MRI (97 citations)
- Identifying Autism Spectrum Disorder With Multi-Site fMRI via Low-Rank Domain Adaptation (24 citations)
- Weakly Supervised Deep Learning for Brain Disease Prognosis Using MRI and Incomplete Clinical Scores (21 citations)
In his most recent research, the most cited papers focused on:
- Artificial intelligence
- Machine learning
- Pattern recognition
Mingxia Liu mostly deals with Artificial intelligence, Pattern recognition, Deep learning, Segmentation and Neuroimaging.
In the field of Artificial intelligence, his study on Feature extraction overlaps with subjects such as Context.
The Pattern recognition study combines topics in areas such as Restricted Boltzmann machine, Pixel, Wiener filter and Self-similarity.
His Deep learning study combines topics from a wide range of disciplines, such as Discriminative model, Artificial neural network, Benchmark, Positron emission tomography and Noise reduction.
His Segmentation study also includes
- Voxel that intertwine with fields like Digitization and Landmark,
- Feature learning which connect with Feature and Image segmentation.
His Neuroimaging study combines topics in areas such as Svm classifier, Machine learning, Feature selection and Ensemble strategy.
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