His primary areas of study are Artificial intelligence, Segmentation, Computer vision, Orthopedic surgery and Surgery. As part of his studies on Artificial intelligence, he often connects relevant areas like Algorithm. His work carried out in the field of Segmentation brings together such families of science as Deep learning and Hausdorff distance.
His Computer vision study integrates concerns from other disciplines, such as Outlier and Mr images. His research investigates the connection between Orthopedic surgery and topics such as Acetabulum that intersect with issues in Pelvis and Radiography. His studies in Surgery integrate themes in fields like Sagittal plane and Navigation system.
Guoyan Zheng spends much of his time researching Artificial intelligence, Computer vision, Segmentation, Surgery and Radiography. His work in Artificial intelligence is not limited to one particular discipline; it also encompasses Pattern recognition. Guoyan Zheng works mostly in the field of Computer vision, limiting it down to topics relating to Imaging phantom and, in certain cases, Similarity measure.
He has included themes like Random forest, Convolutional neural network, Image and Mr images in his Segmentation study. He has researched Surgery in several fields, including Nuclear medicine and Navigation system. The concepts of his Radiography study are interwoven with issues in Orthodontics, Total hip arthroplasty and Pelvis.
His main research concerns Artificial intelligence, Segmentation, Deep learning, Computer vision and Pattern recognition. Guoyan Zheng has included themes like Lumbar and Nonlinear regression in his Artificial intelligence study. His study in the field of Sørensen–Dice coefficient is also linked to topics like Context.
His Deep learning study combines topics in areas such as Algorithm and Big data. His Computer vision study often links to related topics such as Intervertebral disc. His Pattern recognition research incorporates themes from Image, Image translation, Hyperintensity and Test set.
His primary scientific interests are in Segmentation, Artificial intelligence, Pattern recognition, Deep learning and Femoroacetabular impingement. In the field of Segmentation, his study on Sørensen–Dice coefficient overlaps with subjects such as Fully automatic. His Voxel study in the realm of Artificial intelligence connects with subjects such as Negative correlation.
His work on Image segmentation as part of general Pattern recognition study is frequently linked to Context, therefore connecting diverse disciplines of science. His Deep learning research includes elements of Algorithm, Nonlinear regression and Regression. Guoyan Zheng has researched Femoroacetabular impingement in several fields, including 3D reconstruction, Femur, Avascular necrosis, Hip arthroscopy and Random forest.
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Tilt and rotation correction of acetabular version on pelvic radiographs.
Moritz Tannast;Guoyan Zheng;Christoph Anderegg;K Burckhardt.
Clinical Orthopaedics and Related Research (2005)
Crowd Counting with Deep Negative Correlation Learning
Zenglin Shi;Le Zhang;Yun Liu;Xiaofeng Cao.
computer vision and pattern recognition (2018)
A 2D/3D correspondence building method for reconstruction of a patient-specific 3D bone surface model using point distribution models and calibrated X-ray images.
Guoyan Zheng;Sebastian Gollmer;Steffen Schumann;Xiao Dong.
Medical Image Analysis (2009)
What Are the Radiographic Reference Values for Acetabular Under- and Overcoverage?
Moritz Tannast;Markus Hanke;Guoyan Zheng;Simon Damian Steppacher.
Clinical Orthopaedics and Related Research (2015)
Method for establishing a three-dimensional representation of a bone from image data
Guoyan Zheng;Lutz-Peter Nolte.
Why rankings of biomedical image analysis competitions should be interpreted with care
Lena Maier-Hein;Matthias Eisenmann;Annika Reinke;Sinan Onogur.
Nature Communications (2018)
Evaluation of algorithms for Multi-Modality Whole Heart Segmentation: An open-access grand challenge
Xiahai Zhuang;Lei Li;Christian Payer;Darko Stern.
Medical Image Analysis (2019)
Statistical deformable bone models for robust 3D surface extrapolation from sparse data
Kumar T. Rajamani;Martin Andreas Styner;Haydar Talib;Guoyan Zheng.
Medical Image Analysis (2007)
Navigated open-wedge high tibial osteotomy: advantages and disadvantages compared to the conventional technique in a cadaver study
S. Hankemeier;T. Hufner;G. Wang;D. Kendoff.
Knee Surgery, Sports Traumatology, Arthroscopy (2006)
Standardized Assessment of Automatic Segmentation of White Matter Hyperintensities and Results of the WMH Segmentation Challenge
Hugo J. Kuijf;Adria Casamitjana;D. Louis Collins;Mahsa Dadar.
IEEE Transactions on Medical Imaging (2019)
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