Hui Cui
La Trobe University
67 Papers
145 Citations
Hui Cui is an academic researcher from La Trobe University. The author has contributed to research in topics: Computer science & Medicine. The author has an hindex of 10, co-authored 39 publications. Previous affiliations of Hui Cui include University of Sydney.
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Papers
Cascade knowledge diffusion network for skin lesion diagnosis and segmentation
TL;DR: A cascade knowledge diffusion network (CKDNet) to transfer and aggregate knowledge learnt from different tasks to simultaneously boost the performances of classification and segmentation and demonstrates superior performance without using any ensemble approaches or any external datasets.
103
Epileptic Seizure Detection with EEG Textural Features and Imbalanced Classification Based on EasyEnsemble Learning.
TL;DR: Comparisons with 14 published methods demonstrated the improved detection performance for imbalanced EEG signals and the generalizability of the proposed model.
72
Attentional multi-level representation encoding based on convolutional and variance autoencoders for lncRNA-disease association prediction.
TL;DR: Improved recall rates under different top $k$ values demonstrate that the VADLP model is powerful in discovering true disease-related lncRNAs in the top-ranked candidates and outperforms six state-of-the-art lncRNA-disease prediction models with statistical significance.
59
Free-form tumor synthesis in computed tomography images via richer generative adversarial network
TL;DR: Wang et al. as discussed by the authors proposed a new richer generative adversarial network (RicherDG) for free-form 3D tumor/lesion synthesis in computed tomography (CT) images.
41
Learning Deep Spatial Lung Features by 3D Convolutional Neural Network for Early Cancer Detection
Taolin Jin,Hui Cui,Shan Zeng,Xiuying Wang +3 more
- 01 Nov 2017
TL;DR: This work explores the lung cancer early detection capacity by learning from deep spatial lung features by constructing a 3D CNN network architecture constructed with segmented CT lung volumes as training and testing samples.
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