What is he best known for?
The fields of study he is best known for:
- Artificial intelligence
- Machine learning
- Computer vision
Yong Xia focuses on Artificial intelligence, Pattern recognition, Segmentation, Image segmentation and Deep learning.
His Artificial intelligence study frequently links to adjacent areas such as Machine learning.
His Pattern recognition research is multidisciplinary, incorporating elements of Contextual image classification, Feature, Computer vision and Image processing.
Much of his study explores Segmentation relationship to Tumor progression.
His Image segmentation research focuses on Cluster analysis and how it connects with Fuzzy logic, Weighting, Rough set and Euclidean distance.
His Deep learning study integrates concerns from other disciplines, such as Feature extraction and Nodule.
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)
- COVID-19 Screening on Chest X-ray Images Using Deep Learning based Anomaly Detection (169 citations)
- Robust saliency detection via regularized random walks ranking (154 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, Segmentation, Image segmentation and Deep learning.
The study incorporates disciplines such as Machine learning and Computer vision in addition to Artificial intelligence.
His Pattern recognition study also includes fields such as
- Magnetic resonance imaging together with Algorithm,
- Identification which intersects with area such as Dementia.
His Segmentation research includes elements of Brain atlas, Voxel and Medical imaging.
His Image segmentation research incorporates elements of PET-CT and Fuzzy clustering.
His studies in Deep learning integrate themes in fields like Lung cancer and Nodule.
He most often published in these fields:
- Artificial intelligence (99.47%)
- Pattern recognition (74.74%)
- Segmentation (54.74%)
What were the highlights of his more recent work (between 2019-2021)?
- Artificial intelligence (99.47%)
- Pattern recognition (74.74%)
- Segmentation (54.74%)
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 Image segmentation.
Artificial intelligence is frequently linked to Machine learning in his study.
His work on Anomaly detection as part of general Pattern recognition study is frequently connected to Process, therefore bridging the gap between diverse disciplines of science and establishing a new relationship between them.
Many of his research projects under Segmentation are closely connected to Encoder with Encoder, tying the diverse disciplines of science together.
His research in Deep learning intersects with topics in Pyramid, Feature extraction, Relation and Feature vector.
His biological study spans a wide range of topics, including Residual, Pyramid and Benchmark.
Between 2019 and 2021, his most popular works were:
- COVID-19 Screening on Chest X-ray Images Using Deep Learning based Anomaly Detection (169 citations)
- Viral Pneumonia Screening on Chest X-ray Images Using Confidence-Aware Anomaly Detection (69 citations)
- A Mutual Bootstrapping Model for Automated Skin Lesion Segmentation and Classification (28 citations)
In his most recent research, the most cited papers focused on:
- Artificial intelligence
- Machine learning
- Computer vision
Yong Xia mainly investigates Artificial intelligence, Pattern recognition, Deep learning, Segmentation and Convolutional neural network.
His works in Feature, Image segmentation, Anomaly detection, Feature extraction and Binary classification are all subjects of inquiry into Artificial intelligence.
Yong Xia interconnects Retina, Retinal vessel and Fundus in the investigation of issues within Feature.
His Feature extraction research incorporates themes from Contextual image classification, MNIST database, Normalization and Crossover.
His studies deal with areas such as Lesion, Skin cancer, Minimum bounding box, Lymph node and Reinforcement learning as well as Segmentation.
His study in Convolutional neural network is interdisciplinary in nature, drawing from both Artificial neural network, Supervised learning, Pixel and Chest radiograph.
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