What is he best known for?
The fields of study he is best known for:
- Artificial intelligence
- Computer network
- Gene
Jiang Liu mostly deals with Artificial intelligence, Computer vision, Glaucoma, Segmentation and Image segmentation.
His specific area of interest is Artificial intelligence, where he studies Deep learning.
His Deep learning research incorporates elements of Convolutional neural network and Pattern recognition.
His study in Computer vision is interdisciplinary in nature, drawing from both Retinal and Optic nerve.
His Segmentation study combines topics in areas such as Channel and Optical coherence tomography.
His work on Scale-space segmentation as part of general Image segmentation research is frequently linked to Weighted geometric mean, thereby connecting diverse disciplines of science.
His most cited work include:
- CD10 + GPR77 + Cancer-Associated Fibroblasts Promote Cancer Formation and Chemoresistance by Sustaining Cancer Stemness (331 citations)
- Superpixel Classification Based Optic Disc and Optic Cup Segmentation for Glaucoma Screening (329 citations)
- Programming and Inheritance of Parental DNA Methylomes in Mammals (320 citations)
What are the main themes of his work throughout his whole career to date?
Jiang Liu mainly investigates Artificial intelligence, Computer vision, Pattern recognition, Glaucoma and Segmentation.
His Artificial intelligence research includes elements of Retinal, Fundus and Optical coherence tomography.
His Optical coherence tomography research includes themes of Image quality and Speckle noise.
His studies in Computer vision integrate themes in fields like Retina and Optic disc, Optic cup.
His Pattern recognition research is multidisciplinary, incorporating elements of Artificial neural network, Contextual image classification and Feature.
His Cup-to-disc ratio and Open angle glaucoma study are his primary interests in Glaucoma.
He most often published in these fields:
- Artificial intelligence (53.30%)
- Computer vision (34.38%)
- Pattern recognition (20.27%)
What were the highlights of his more recent work (between 2019-2021)?
- Artificial intelligence (53.30%)
- Optical coherence tomography (15.77%)
- Pattern recognition (20.27%)
In recent papers he was focusing on the following fields of study:
Artificial intelligence, Optical coherence tomography, Pattern recognition, Segmentation and Computer vision are his primary areas of study.
His research links Retinal with Artificial intelligence.
His Optical coherence tomography research integrates issues from Image quality, Artificial neural network, Pixel, Choroid and Glaucoma.
Jiang Liu interconnects Focus, Similarity and Feature in the investigation of issues within Pattern recognition.
In general Segmentation, his work in Image segmentation is often linked to Curvilinear coordinates linking many areas of study.
His work on Speckle noise as part of general Computer vision study is frequently linked to Data acquisition, bridging the gap between disciplines.
Between 2019 and 2021, his most popular works were:
- DNA of neutrophil extracellular traps promotes cancer metastasis via CCDC25 (58 citations)
- Calcitriol, the active form of vitamin D, is a promising candidate for COVID-19 prophylaxis (23 citations)
- Angle-Closure Detection in Anterior Segment OCT Based on Multilevel Deep Network (23 citations)
In his most recent research, the most cited papers focused on:
- Artificial intelligence
- Computer network
- Gene
Jiang Liu mainly focuses on Artificial intelligence, Pattern recognition, Optical coherence tomography, Deep learning and Segmentation.
His Artificial intelligence research is multidisciplinary, relying on both Retinal and Computer vision.
The various areas that Jiang Liu examines in his Computer vision study include Retina, Noise and Optic cup.
His Pattern recognition study combines topics from a wide range of disciplines, such as Image, Feature and Diabetic macular edema.
He has researched Optical coherence tomography in several fields, including Leverage, Pixel, Anomaly detection, Choroid and Glaucoma.
In his work, Normalization, Kernel, Channel and Optical imaging is strongly intertwined with Convolutional neural network, which is a subfield of Deep learning.
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