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Guang Yang

Researcher at National Institutes of Health

Publications -  191
Citations -  7461

Guang Yang is an academic researcher from National Institutes of Health. The author has contributed to research in topics: Computer science & Segmentation. The author has an hindex of 29, co-authored 163 publications receiving 4337 citations. Previous affiliations of Guang Yang include University College London & Imperial College London.

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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

DAGAN: Deep De-Aliasing Generative Adversarial Networks for Fast Compressed Sensing MRI Reconstruction

Guang Yang, +10 more
- 01 Jun 2018 - 
TL;DR: This paper provides a deep learning-based strategy for reconstruction of CS-MRI, and bridges a substantial gap between conventional non-learning methods working only on data from a single image, and prior knowledge from large training data sets.
Book ChapterDOI

Automatic Brain Tumor Detection and Segmentation Using U-Net Based Fully Convolutional Networks

Hao Dong, +4 more
TL;DR: Wang et al. as discussed by the authors proposed a fully automatic method for brain tumor segmentation, which was developed using U-Net based deep convolutional networks, and evaluated on Multimodal Brain Tumor Image Segmentation (BRATS 2015) datasets, which contain 220 high-grade brain tumor and 54 low-grade tumor cases.
Posted Content

Automatic Brain Tumor Detection and Segmentation Using U-Net Based Fully Convolutional Networks

Hao Dong, +4 more
- 10 May 2017 - 
TL;DR: This study proposes a fully automatic method for brain tumor segmentation, which is developed using U-Net based deep convolutional networks, which was evaluated on Multimodal Brain Tumor Image Segmentation (BRATS 2015) datasets, showing that it can obtain promising segmentation efficiently.
Journal ArticleDOI

Weakly Supervised Deep Learning for COVID-19 Infection Detection and Classification From CT Images

Shaoping Hu, +11 more
- 29 Jun 2020 - 
TL;DR: This study proposes a weakly supervised deep learning strategy for detecting and classifying COVID-19 infection from CT images that can minimise the requirements of manual labelling of CT images but still be able to obtain accurate infection detection and distinguish CO VID-19 from non-COVID- 19 cases.