Related references
Note: Only part of the references are listed.Recurrent generative adversarial network for learning imbalanced medical image semantic segmentation
Mina Rezaei et al.
MULTIMEDIA TOOLS AND APPLICATIONS (2020)
Augmenting data with GANs to segment melanoma skin lesions
Federico Pollastri et al.
MULTIMEDIA TOOLS AND APPLICATIONS (2020)
Deep CT to MR Synthesis Using Paired and Unpaired Data
Cheng-Bin Jin et al.
SENSORS (2019)
Image Synthesis in Multi-Contrast MRI With Conditional Generative Adversarial Networks
Salman U. H. Dar et al.
IEEE TRANSACTIONS ON MEDICAL IMAGING (2019)
A semantic-based video scene segmentation using a deep neural network
Hyesung Ji et al.
JOURNAL OF INFORMATION SCIENCE (2019)
Generative adversarial network in medical imaging: A review
Xin Yi et al.
MEDICAL IMAGE ANALYSIS (2019)
Optimized generation of high-resolution phantom images using cGAN: Application to quantification of Ki67 breast cancer images
Caglar Senaras et al.
PLOS ONE (2018)
Early Diagnosis of Dementia from Clinical Data by Machine Learning Techniques
Aram So et al.
APPLIED SCIENCES-BASEL (2017)
PACS for Bhutan: a cost effective open source architecture for emerging countries
Osman Ratib et al.
INSIGHTS INTO IMAGING (2016)
Parameter optimization of improved fuzzy c-means clustering algorithm for brain MR image segmentation
Mohamad Forouzanfar et al.
ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE (2010)
Role of radiography, MRI and FDG-PET/CT in diagnosing, staging and therapeutical evaluation of patients with multiple myeloma
Susanne Lutje et al.
ANNALS OF HEMATOLOGY (2009)
MR procedures: Biologic effects, safety, and patient care
FG Shellock et al.
RADIOLOGY (2004)