Z
Z. Jane Wang
Researcher at University of British Columbia
Publications - 283
Citations - 7675
Z. Jane Wang is an academic researcher from University of British Columbia. The author has contributed to research in topics: Computer science & Convolutional neural network. The author has an hindex of 33, co-authored 266 publications receiving 5280 citations. Previous affiliations of Z. Jane Wang include University of Maryland, College Park & Siemens.
Papers
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Image Fusion With Convolutional Sparse Representation
TL;DR: A recently emerged signal decomposition model known as convolutional sparse representation (CSR) is introduced into image fusion to address this problem, motivated by the observation that the CSR model can effectively overcome the above two drawbacks.
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Deep learning for pixel-level image fusion: Recent advances and future prospects
TL;DR: This survey paper presents a systematic review of the DL-based pixel-level image fusion literature, summarized the main difficulties that exist in conventional image fusion research and discussed the advantages that DL can offer to address each of these problems.
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A CNN Regression Approach for Real-Time 2D/3D Registration
Shun Miao,Z. Jane Wang,Rui Liao +2 more
TL;DR: The proposed CNN regression approach has been quantitatively evaluated on 3 potential clinical applications, demonstrating its significant advantage in providing highly accurate real-time 2-D/3-D registration with a significantly enlarged capture range when compared to intensity-based methods.
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Median Filtering Forensics Based on Convolutional Neural Networks
TL;DR: This work proposes a median filtering detection method based on convolutional neural networks (CNNs), which can automatically learn and obtain features directly from the image and achieves significant performance improvements, especially in the cut-and-paste forgery detection.
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3D CNN Based Automatic Diagnosis of Attention Deficit Hyperactivity Disorder Using Functional and Structural MRI
TL;DR: A deep learning-based ADHD classification method via 3-D convolutional neural networks (CNNs) applied to MRI scans is developed, suggesting that multi-modality classification will be a promising direction to find potential neuroimaging biomarkers of neurodevelopment disorders.