L
Luping Zhou
Researcher at University of Sydney
Publications - 165
Citations - 4869
Luping Zhou is an academic researcher from University of Sydney. The author has contributed to research in topics: Computer science & Discriminative model. The author has an hindex of 24, co-authored 130 publications receiving 3373 citations. Previous affiliations of Luping Zhou include Australian National University & University of North Carolina at Chapel Hill.
Papers
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Journal ArticleDOI
Multimodal Classification of Alzheimer’s Disease and Mild Cognitive Impairment
TL;DR: Three modalities of biomarkers are proposed to combine, i.e., MRI, FDG-PET, and CSF biomarkers, to discriminate between AD (or MCI) and healthy controls, using a kernel combination method, and shows considerably better performance, compared to the case of using an individual modality of biomarker.
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3D conditional generative adversarial networks for high-quality PET image estimation at low dose
Yan Wang,Biting Yu,Lei Wang,Chen Zu,David S. Lalush,Weili Lin,Xi Wu,Jiliu Zhou,Dinggang Shen,Dinggang Shen,Luping Zhou,Luping Zhou +11 more
TL;DR: Experimental results show that the proposed 3D c‐GANs method outperforms the benchmark methods and achieves much better performance than the state‐of‐the‐art methods in both qualitative and quantitative measures.
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HEp-2 Cell Image Classification With Deep Convolutional Neural Networks
TL;DR: The results on benchmark datasets demonstrate that the proposed framework can effectively outperform existing models by properly applying data augmentation, and the CNN-based framework has excellent adaptability across different datasets, which is highly desirable for cell image classification under varying laboratory settings.
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Discriminant analysis of longitudinal cortical thickness changes in Alzheimer's disease using dynamic and network features
TL;DR: The recently developed 4 D (spatial+temporal) thickness measuring algorithm finds that the 4 clinical groups demonstrate very similar spatial distribution of grey matter (GM) loss on cortex, and indicates the degenerated wiring efficiency of the brain network due to AD.
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Ea-GANs: Edge-Aware Generative Adversarial Networks for Cross-Modality MR Image Synthesis
TL;DR: The experimental results demonstrate that the proposed edge-aware generative adversarial networks (Ea-GANs) outperform multiple state-of-the-art methods for cross-modality MR image synthesis in both qualitative and quantitative measures.