What is he best known for?
The fields of study he is best known for:
- Artificial intelligence
- Computer vision
- Radiology
His main research concerns Artificial intelligence, Segmentation, Computer vision, Deep learning and Pattern recognition.
The Artificial intelligence study which covers Magnetic resonance imaging that intersects with Radiation therapy and Brain tumor segmentation.
His Segmentation study frequently draws connections to other fields, such as Machine learning.
The study incorporates disciplines such as White matter, Partial volume, Sparse approximation and Brain tissue in addition to Computer vision.
His biological study spans a wide range of topics, including Image, Radiology, Feature learning, Stage and Sampling.
His research investigates the connection with Pattern recognition and areas like Image fusion which intersect with concerns in Nuclear medicine.
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)
- Lung Infection Quantification of COVID-19 in CT Images with Deep Learning. (230 citations)
- LINKS: Learning-based multi-source IntegratioN frameworK for Segmentation of infant brain images (153 citations)
What are the main themes of his work throughout his whole career to date?
His primary areas of study are Artificial intelligence, Segmentation, Pattern recognition, Computer vision and Random forest.
Image segmentation, Voxel, Image, Deep learning and Image registration are subfields of Artificial intelligence in which his conducts study.
In his research, Pathology is intimately related to Prostate, which falls under the overarching field of Segmentation.
His Pattern recognition research integrates issues from Feature and Regression.
His research investigates the connection between Computer vision and topics such as Magnetic resonance imaging that intersect with problems in Radiation therapy, Learning based, Positron emission tomography and Image quality.
His study in Random forest is interdisciplinary in nature, drawing from both Classifier, Radiology and Mr images.
He most often published in these fields:
- Artificial intelligence (88.08%)
- Segmentation (53.64%)
- Pattern recognition (50.99%)
What were the highlights of his more recent work (between 2020-2021)?
- Artificial intelligence (88.08%)
- Pattern recognition (50.99%)
- Segmentation (53.64%)
In recent papers he was focusing on the following fields of study:
His primary areas of investigation include Artificial intelligence, Pattern recognition, Segmentation, Deep learning and Radiology.
His Robustness study, which is part of a larger body of work in Artificial intelligence, is frequently linked to Vertex, bridging the gap between disciplines.
His Pattern recognition study combines topics in areas such as Image, Radiomics, Ct imaging and Identification.
The various areas that Yaozong Gao examines in his Segmentation study include Nodule detection, Ground truth, Test set, Time course and Enhanced ct.
The Deep learning study combines topics in areas such as Chest ct, Lung and Computed tomography.
His study in the field of Medical imaging and Voxel is also linked to topics like Spatial distribution pattern.
Between 2020 and 2021, his most popular works were:
- Large-scale screening of COVID-19 from community acquired pneumonia using infection size-aware classification. (35 citations)
- The state of the art in kidney and kidney tumor segmentation in contrast-enhanced CT imaging: Results of the KiTS19 challenge. (23 citations)
- Joint prediction and time estimation of COVID-19 developing severe symptoms using chest CT scan. (19 citations)
In his most recent research, the most cited papers focused on:
- Artificial intelligence
- Computer vision
- Statistics
His primary scientific interests are in Artificial intelligence, Pattern recognition, Feature selection, Severity of illness and Regression.
His studies in Pattern recognition integrate themes in fields like Random forest and Test set.
His Random forest research is multidisciplinary, relying on both Image processing, Retrospective cohort study, Generalizability theory and Decision tree.
His studies deal with areas such as Ground truth, Deep learning, Medical image computing and Enhanced ct as well as Test set.
His Ground truth research is multidisciplinary, incorporating perspectives in Segmentation and Contrast.
His research in Feature selection intersects with topics in Correlation coefficient, Predictive value of tests, Outlier and Disease progression.
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