Jianhua Yao focuses on Artificial intelligence, Radiology, Convolutional neural network, Deep learning and Computer vision. Jianhua Yao usually deals with Artificial intelligence and limits it to topics linked to Pattern recognition and Cognitive neuroscience of visual object recognition. The study incorporates disciplines such as Computer aided detection, Colonoscopy, Virtual colonoscopy and Lung in addition to Radiology.
His study looks at the relationship between Convolutional neural network and topics such as Image, which overlap with Machine learning and Recurrent neural network. Jianhua Yao interconnects Picture archiving and communication system, Concordance, Database and Medical imaging in the investigation of issues within Deep learning. His Computer vision study integrates concerns from other disciplines, such as Computer integrated surgery and Fuzzy clustering.
His primary areas of investigation include Artificial intelligence, Radiology, Segmentation, Pattern recognition and Computer vision. His Artificial intelligence study combines topics in areas such as Virtual colonoscopy and Machine learning. His work deals with themes such as False positive paradox, Lung and Nuclear medicine, which intersect with Radiology.
His study ties his expertise on Contextual image classification together with the subject of Pattern recognition. His Convolutional neural network research integrates issues from Artificial neural network and Medical imaging. His studies in Computed tomography integrate themes in fields like Anatomy, Vertebra and Radiography.
Jianhua Yao mainly focuses on Artificial intelligence, Pattern recognition, Deep learning, Segmentation and Convolutional neural network. His studies in Artificial intelligence integrate themes in fields like Machine learning and Computer vision. The concepts of his Machine learning study are interwoven with issues in Classifier and Data set.
His Pattern recognition research incorporates themes from Breast cancer, Cytoplasm, Digital pathology and Medical imaging. Jianhua Yao interconnects Colorectal cancer and Computed tomography in the investigation of issues within Deep learning. His research integrates issues of Surgical planning and Radiology in his study of Segmentation.
His primary scientific interests are in Artificial intelligence, Deep learning, Pattern recognition, Segmentation and Nuclear medicine. Jianhua Yao combines topics linked to Machine learning with his work on Artificial intelligence. His studies deal with areas such as Contextual image classification, Stain, Digital pathology and Medical imaging as well as Pattern recognition.
His Segmentation study incorporates themes from Surgical planning and Radiology. He has included themes like Cyst, Lymphangioleiomyomatosis, Lung and Abdominal ct in his Nuclear medicine study. His research investigates the connection with Artificial neural network and areas like Pascal which intersect with concerns in Training set.
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Deep Convolutional Neural Networks for Computer-Aided Detection: CNN Architectures, Dataset Characteristics and Transfer Learning
Hoo-Chang Shin;Holger R. Roth;Mingchen Gao;Le Lu.
IEEE Transactions on Medical Imaging (2016)
Polyps: Linear and Volumetric Measurement at CT Colonography
Srinath C. Yeshwant;Ronald M. Summers;Jianhua Yao;Daniel S. Brickman.
Improving Computer-Aided Detection Using Convolutional Neural Networks and Random View Aggregation
Holger R. Roth;Le Lu;Jiamin Liu;Jianhua Yao.
IEEE Transactions on Medical Imaging (2016)
Computed tomographic virtual colonoscopy computer-aided polyp detection in a screening population.
Ronald M. Summers;Jianhua Yao;Perry J. Pickhardt;Marek Franaszek.
Danazol Treatment for Telomere Diseases.
Danielle M Townsley;Bogdan Dumitriu;Delong Liu;Angélique Biancotto.
The New England Journal of Medicine (2016)
Medical Image Segmentation by Combining Graph Cuts and Oriented Active Appearance Models
Xinjian Chen;J. K. Udupa;U. Bagci;Ying Zhuge.
IEEE Transactions on Image Processing (2012)
Computer-integrated revision total hip replacement surgery: concept and preliminary results.
Russell H. Taylor;Leo Joskowicz;Bill Williamson;André Guéziec.
Medical Image Analysis (1999)
DeepPap: Deep Convolutional Networks for Cervical Cell Classification.
Ling Zhang;Le Lu;Isabella Nogues;Ronald M. Summers.
IEEE Journal of Biomedical and Health Informatics (2017)
Joint segmentation of anatomical and functional images: Applications in quantification of lesions from PET, PET-CT, MRI-PET, and MRI-PET-CT images
Ulas Bagci;Jayaram K. Udupa;Neil Mendhiratta;Neil Mendhiratta;Brent Foster.
Medical Image Analysis (2013)
Automated spinal column extraction and partitioning
Jianhua Yao;S.D. O'Connor;R.M. Summers.
international symposium on biomedical imaging (2006)
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