What is he best known for?
The fields of study he is best known for:
- Artificial intelligence
- Machine learning
- Internal medicine
Wenjia Bai mostly deals with Artificial intelligence, Segmentation, Image segmentation, Artificial neural network and Pattern recognition.
His biological study spans a wide range of topics, including Machine learning and Computer vision.
His work deals with themes such as Magnetic resonance imaging and Medical imaging, which intersect with Segmentation.
Wenjia Bai has included themes like Algorithm, Angiology and Atrial fibrillation, Catheter ablation in his Magnetic resonance imaging study.
His work investigates the relationship between Image segmentation and topics such as Convolutional neural network that intersect with problems in Conditional random field, GrabCut, Image resolution and Minimum bounding box.
He combines subjects such as Interpretability and Computed tomography with his study of Artificial neural network.
His most cited work include:
- Identifying the Best Machine Learning Algorithms for Brain Tumor Segmentation, Progression Assessment, and Overall Survival Prediction in the BRATS Challenge (493 citations)
- Anatomically Constrained Neural Networks (ACNNs): Application to Cardiac Image Enhancement and Segmentation (333 citations)
- Automated cardiovascular magnetic resonance image analysis with fully convolutional networks (224 citations)
What are the main themes of his work throughout his whole career to date?
Artificial intelligence, Segmentation, Pattern recognition, Image segmentation and Computer vision are his primary areas of study.
His studies deal with areas such as Machine learning and Magnetic resonance imaging, Mr images as well as Artificial intelligence.
His work carried out in the field of Segmentation brings together such families of science as Image quality, Ground truth, Convolutional neural network and Medical imaging.
His work on Discriminative model as part of general Pattern recognition research is frequently linked to Key, thereby connecting diverse disciplines of science.
His Scale-space segmentation study in the realm of Image segmentation connects with subjects such as Quality, Set and Atlas.
His work on Respiratory motion correction, Cardiac motion, Motion and Plane as part of general Computer vision study is frequently linked to Motion, therefore connecting diverse disciplines of science.
He most often published in these fields:
- Artificial intelligence (72.62%)
- Segmentation (52.38%)
- Pattern recognition (37.50%)
What were the highlights of his more recent work (between 2019-2021)?
- Artificial intelligence (72.62%)
- Segmentation (52.38%)
- Image segmentation (26.19%)
In recent papers he was focusing on the following fields of study:
His main research concerns Artificial intelligence, Segmentation, Image segmentation, Pattern recognition and Machine learning.
He applies his multidisciplinary studies on Artificial intelligence and Uncertainty estimation in his research.
His research in Segmentation intersects with topics in Image quality, Temporal database, Convolutional neural network and Cardiac magnetic resonance imaging.
His research investigates the connection with Image segmentation and areas like Medical imaging which intersect with concerns in Annotation.
His studies in Pattern recognition integrate themes in fields like Cardiac motion, Visualization and Object.
In general Machine learning study, his work on Supervised learning, Semi-supervised learning and Self training often relates to the realm of Scheme, thereby connecting several areas of interest.
Between 2019 and 2021, his most popular works were:
- Deep Learning for Cardiac Image Segmentation: A Review. (91 citations)
- Fully Automated, Quality-Controlled Cardiac Analysis From CMR: Validation and Large-Scale Application to Characterize Cardiac Function. (43 citations)
- Fully Automated, Quality-Controlled Cardiac Analysis From CMR: Validation and Large-Scale Application to Characterize Cardiac Function. (43 citations)
In his most recent research, the most cited papers focused on:
- Artificial intelligence
- Machine learning
- Internal medicine
His primary areas of investigation include Segmentation, Internal medicine, Cardiology, Mendelian randomization and Genome-wide association study.
The various areas that Wenjia Bai examines in his Segmentation study include Image quality, Ventricular function, Convolutional neural network and Reference values.
His study on Cardiac function curve, Stenosis and Perfusion scanning is often connected to Fully automated as part of broader study in Internal medicine.
His work on Cardiac imaging, Perfusion, Coronary artery disease and Blood flow is typically connected to Fractional flow reserve as part of general Cardiology study, connecting several disciplines of science.
His research in Disease tackles topics such as Biobank which are related to areas like Magnetic resonance imaging.
His study on Interpretability is covered under Artificial intelligence.
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