What is he best known for?
The fields of study he is best known for:
- Artificial intelligence
- Computer vision
- Machine learning
His main research concerns Artificial intelligence, Pattern recognition, Computer vision, Image segmentation and Segmentation.
His study involves Object detection, Deep learning, Pattern recognition, Feature vector and Unsupervised learning, a branch of Artificial intelligence.
His research integrates issues of Ground truth and Image noise in his study of Feature vector.
The various areas that Pingkun Yan examines in his Pattern recognition study include Image resolution, Image and Iterative reconstruction.
The concepts of his Image segmentation study are interwoven with issues in Kadir–Brady saliency detector and Seam carving.
In his research, Convolution and Boundary is intimately related to Convolutional neural network, which falls under the overarching field of Segmentation.
His most cited work include:
- Low-Dose CT Image Denoising Using a Generative Adversarial Network With Wasserstein Distance and Perceptual Loss (467 citations)
- Magnetic Resonance Imaging/Ultrasound Fusion Guided Prostate Biopsy Improves Cancer Detection Following Transrectal Ultrasound Biopsy and Correlates With Multiparametric Magnetic Resonance Imaging (370 citations)
- Manifold Regularized Sparse NMF for Hyperspectral Unmixing (246 citations)
What are the main themes of his work throughout his whole career to date?
His primary areas of study are Artificial intelligence, Computer vision, Pattern recognition, Deep learning and Image segmentation.
Pingkun Yan focuses mostly in the field of Artificial intelligence, narrowing it down to topics relating to Machine learning and, in certain cases, Lung cancer.
His research on Computer vision frequently links to adjacent areas such as Ultrasound.
His Pattern recognition research is multidisciplinary, incorporating elements of Image and Feature.
His work deals with themes such as Artificial neural network, Image noise, Medical imaging and Fluorescence-lifetime imaging microscopy, which intersect with Deep learning.
In Image noise, Pingkun Yan works on issues like Feature vector, which are connected to Ground truth.
He most often published in these fields:
- Artificial intelligence (73.85%)
- Computer vision (39.45%)
- Pattern recognition (35.32%)
What were the highlights of his more recent work (between 2019-2021)?
- Artificial intelligence (73.85%)
- Deep learning (22.48%)
- Computer vision (39.45%)
In recent papers he was focusing on the following fields of study:
His primary areas of investigation include Artificial intelligence, Deep learning, Computer vision, Medical imaging and Machine learning.
Pingkun Yan applies his multidisciplinary studies on Artificial intelligence and Context in his research.
His Deep learning research is multidisciplinary, incorporating perspectives in Segmentation, Pattern recognition, Artificial neural network, Lung cancer screening and Image.
Pingkun Yan usually deals with Pattern recognition and limits it to topics linked to Pyramid and Leverage.
His study in the fields of Multimodality image fusion under the domain of Computer vision overlaps with other disciplines such as Image-Guided Biopsy.
The study incorporates disciplines such as Functional Brain Imaging, Key, Robustness and Training set in addition to Machine learning.
Between 2019 and 2021, his most popular works were:
- Deep learning in medical image registration: a survey (111 citations)
- Boundary-Weighted Domain Adaptive Neural Network for Prostate MR Image Segmentation (52 citations)
- Multi-Organ Segmentation Over Partially Labeled Datasets With Multi-Scale Feature Abstraction (16 citations)
In his most recent research, the most cited papers focused on:
- Artificial intelligence
- Computer vision
- Machine learning
Pingkun Yan focuses on Artificial intelligence, Deep learning, Medical imaging, Segmentation and Convolutional neural network.
Pingkun Yan combines subjects such as Effective diffusion coefficient and Biopsy with his study of Artificial intelligence.
In his work, Feature is strongly intertwined with Feature extraction, which is a subfield of Deep learning.
His work carried out in the field of Medical imaging brings together such families of science as Predictive value of tests, Medical physics and Intensive care unit, Intensive care medicine.
His work on Image segmentation as part of general Segmentation study is frequently connected to Gallbladder and Esophagus, therefore bridging the gap between diverse disciplines of science and establishing a new relationship between them.
His Image segmentation research is under the purview of Pattern recognition.
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