J
Jingfan Fan
Researcher at Beijing Institute of Technology
Publications - 93
Citations - 1248
Jingfan Fan is an academic researcher from Beijing Institute of Technology. The author has contributed to research in topics: Computer science & Medicine. The author has an hindex of 14, co-authored 71 publications receiving 778 citations. Previous affiliations of Jingfan Fan include University of North Carolina at Chapel Hill & Beihang University.
Papers
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Journal ArticleDOI
BIRNet: Brain image registration using dual-supervised fully convolutional networks.
TL;DR: Zhang et al. as discussed by the authors designed a fully convolutional network that is subject to dual-guidance: ground-truth guidance using deformation fields obtained by an existing registration method; and image dissimilarity guidance using the difference between the images after registration.
Book ChapterDOI
Adversarial Similarity Network for Evaluating Image Alignment in Deep Learning based Registration
TL;DR: An unsupervised adversarial similarity network for image registration is introduced that is promising in both accuracy and efficiency compared with state-of-the-art registration methods, including those based on deep learning.
Journal ArticleDOI
Adversarial learning for mono- or multi-modal registration
TL;DR: Experiments indicate that the proposed unsupervised adversarial similarity network for image registration yields promising registration performance in accuracy, efficiency and generalizability compared with state-of-the-art registration methods, including those based on deep learning.
Journal ArticleDOI
Local statistics and non-local mean filter for speckle noise reduction in medical ultrasound image
TL;DR: This study combines local statistics with the NLM filter to reduce speckle in ultrasound images and demonstrates that the proposed method outperforms the original NLM, as well as many previously developed methods.
Posted Content
BIRNet: Brain Image Registration Using Dual-Supervised Fully Convolutional Networks
TL;DR: A fully convolutional network is designed that is subject to dual‐guidance: ground‐truth guidance using deformation fields obtained by an existing registration method; and image dissimilarity guidance using the difference between the images after registration.