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Ruixuan Wang

Researcher at Sun Yat-sen University

Publications -  89
Citations -  3190

Ruixuan Wang is an academic researcher from Sun Yat-sen University. The author has contributed to research in topics: Computer science & Deep learning. The author has an hindex of 14, co-authored 61 publications receiving 1862 citations. Previous affiliations of Ruixuan Wang include National University of Singapore & University of Dundee.

Papers
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Posted ContentDOI

Identifying the Best Machine Learning Algorithms for Brain Tumor Segmentation, Progression Assessment, and Overall Survival Prediction in the BRATS Challenge

Spyridon Bakas, +438 more
- 05 Nov 2018 - 
TL;DR: This study assesses the state-of-the-art machine learning methods used for brain tumor image analysis in mpMRI scans, during the last seven instances of the International Brain Tumor Segmentation (BraTS) challenge, i.e., 2012-2018, and investigates the challenge of identifying the best ML algorithms for each of these tasks.
Journal ArticleDOI

Deep learning Enables Accurate Diagnosis of Novel Coronavirus (COVID-19) with CT images.

Ying Song, +12 more
- 11 Mar 2021 - 
TL;DR: Wang et al. as mentioned in this paper developed a deep learning-based CT diagnosis system to identify patients with COVID-19, which achieved an AUC of 0.99, recall (sensitivity) of 0.,93, and precision of 0,96.
Posted ContentDOI

Deep learning Enables Accurate Diagnosis of Novel Coronavirus (COVID-19) with CT images

Song Ying, +12 more
- 25 Feb 2020 - 
TL;DR: A deep learning-based CT diagnosis system (DeepPneumonia) was developed and showed that the established models can achieve a rapid and accurate identification of COVID-19 in human samples, thereby allowing identification of patients.
Journal ArticleDOI

Fully convolutional network ensembles for white matter hyperintensities segmentation in MR images.

Hongwei Li, +6 more
- 01 Dec 2018 - 
TL;DR: The effectiveness and generalization capability of the proposed system show its potential for real‐world clinical practice and are the highest achieved in the challenge, suggesting the proposed method is the state‐of‐the‐art.
Book ChapterDOI

Boundary-aware fully convolutional network for brain tumor segmentation

Haocheng Shen, +3 more
TL;DR: A novel, multi-task, fully convolutional network (FCN) architecture for automatic segmentation of brain tumor achieves improved segmentation performance by incorporating boundary information directly into the loss function.