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.
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.
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.
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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CE-Net: Context Encoder Network for 2D Medical Image Segmentation
Zaiwang Gu;Jun Cheng;Huazhu Fu;Kang Zhou.
IEEE Transactions on Medical Imaging (2019)
Superpixel Classification Based Optic Disc and Optic Cup Segmentation for Glaucoma Screening
Jun Cheng;Jiang Liu;Yanwu Xu;Fengshou Yin.
IEEE Transactions on Medical Imaging (2013)
Joint Optic Disc and Cup Segmentation Based on Multi-Label Deep Network and Polar Transformation
Huazhu Fu;Jun Cheng;Yanwu Xu;Damon Wing Kee Wong.
IEEE Transactions on Medical Imaging (2018)
DeepVessel: Retinal Vessel Segmentation via Deep Learning and Conditional Random Field
Huazhu Fu;Yanwu Xu;Stephen Lin;Damon Wing Kee Wong.
medical image computing and computer assisted intervention (2016)
Glaucoma detection based on deep convolutional neural network
Xiangyu Chen;Yanwu Xu;Damon Wing Kee Wong;Tien Yin Wong.
international conference of the ieee engineering in medicine and biology society (2015)
ORIGA -light : An online retinal fundus image database for glaucoma analysis and research
Zhuo Zhang;Feng Shou Yin;Jiang Liu;Wing Kee Wong.
international conference of the ieee engineering in medicine and biology society (2010)
Retinal vessel segmentation via deep learning network and fully-connected conditional random fields
Huazhu Fu;Yanwu Xu;Damon Wing Kee Wong;Jiang Liu.
international symposium on biomedical imaging (2016)
Disc-Aware Ensemble Network for Glaucoma Screening From Fundus Image
Huazhu Fu;Jun Cheng;Yanwu Xu;Changqing Zhang.
IEEE Transactions on Medical Imaging (2018)
Level-set based automatic cup-to-disc ratio determination using retinal fundus images in ARGALI
D. W. K. Wong;J. Liu;J.H. Lim;X. Jia.
international conference of the ieee engineering in medicine and biology society (2008)
Automated segmentation of optic disc and optic cup in fundus images for glaucoma diagnosis
Fengshou Yin;Jiang Liu;Damon Wing Kee Wong;Ngan Meng Tan.
computer-based medical systems (2012)
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