What is he best known for?
The fields of study he is best known for:
- Artificial intelligence
- Internal medicine
- Cancer
His primary areas of investigation include Artificial intelligence, Pattern recognition, Computer vision, Deep learning and Image registration.
Qian Wang has included themes like Magnetic resonance imaging and Atlas in his Artificial intelligence study.
His work in the fields of Pattern recognition, such as Support vector machine and Brain segmentation, overlaps with other areas such as Expectation–maximization algorithm and Joint probability distribution.
His Computer vision study which covers Point that intersects with Diffeomorphism, Matching, Hammer and Minimum spanning tree.
His Deep learning research includes elements of Radiology, Feature learning, Stage, Sampling and Receiver operating characteristic.
His Image registration research integrates issues from Ground truth, Similarity, Feature detection and Feature.
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)
- Medical Image Synthesis with Context-Aware Generative Adversarial Networks. (337 citations)
- Review of Artificial Intelligence Techniques in Imaging Data Acquisition, Segmentation, and Diagnosis for COVID-19 (297 citations)
What are the main themes of his work throughout his whole career to date?
Qian Wang mainly investigates Artificial intelligence, Pattern recognition, Computer vision, Deep learning and Image.
When carried out as part of a general Artificial intelligence research project, his work on Image registration, Segmentation and Voxel is frequently linked to work in Field, therefore connecting diverse disciplines of study.
A large part of his Segmentation studies is devoted to Image segmentation.
The study incorporates disciplines such as Cluster analysis, Feature and Robustness in addition to Pattern recognition.
His work deals with themes such as Point, Magnetic resonance imaging, Mr images and Atlas, which intersect with Computer vision.
His research is interdisciplinary, bridging the disciplines of Medical imaging and Deep learning.
He most often published in these fields:
- Artificial intelligence (57.89%)
- Pattern recognition (31.27%)
- Computer vision (23.53%)
What were the highlights of his more recent work (between 2019-2021)?
- Artificial intelligence (57.89%)
- Deep learning (15.17%)
- Segmentation (12.69%)
In recent papers he was focusing on the following fields of study:
The scientist’s investigation covers issues in Artificial intelligence, Deep learning, Segmentation, Pattern recognition and Medical imaging.
His Artificial intelligence research incorporates themes from Machine learning and Computer vision.
His research in Deep learning intersects with topics in Image quality, Digital pathology, Brain magnetic resonance imaging and Multi modal fusion.
His studies deal with areas such as Early lung cancer, Voxel, Nodule and Alias as well as Segmentation.
Qian Wang has researched Pattern recognition in several fields, including Artificial neural network, Focus, Magnetic resonance imaging and Robustness.
His Medical imaging research is multidisciplinary, relying on both Supervised learning, Similarity and Precision medicine.
Between 2019 and 2021, his most popular works were:
- Review of Artificial Intelligence Techniques in Imaging Data Acquisition, Segmentation, and Diagnosis for COVID-19 (297 citations)
- Dual-Sampling Attention Network for Diagnosis of COVID-19 From Community Acquired Pneumonia (75 citations)
- A Multi-Organ Nucleus Segmentation Challenge (40 citations)
In his most recent research, the most cited papers focused on:
- Artificial intelligence
- Internal medicine
- Cancer
Qian Wang focuses on Artificial intelligence, Segmentation, Medical imaging, Deep learning and Image segmentation.
His research integrates issues of Stage, Cancer therapy, Machine learning and Pattern recognition in his study of Artificial intelligence.
His Pattern recognition research focuses on Voxel and how it relates to Annotation and Supervised learning.
Qian Wang combines subjects such as Similarity and Minimum bounding box with his study of Medical imaging.
His study in Deep learning is interdisciplinary in nature, drawing from both Community-acquired pneumonia, Digital pathology, Medical diagnosis, Focus and Test set.
Qian Wang interconnects Feature, Image registration, Feature learning, Principles of learning and Data science in the investigation of issues within Image segmentation.
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