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Luyan Liu

Researcher at Tencent

Publications -  17
Citations -  1723

Luyan Liu is an academic researcher from Tencent. The author has contributed to research in topics: Deep learning & Segmentation. The author has an hindex of 8, co-authored 17 publications receiving 1133 citations. Previous affiliations of Luyan Liu include University of North Carolina at Chapel Hill & Shanghai Jiao Tong University.

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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.
Book ChapterDOI

3D Deep Learning for Multi-modal Imaging-Guided Survival Time Prediction of Brain Tumor Patients.

Dong Nie, +4 more
TL;DR: 3D convolutional neural networks are adopted and a new network architecture is proposed for using multi-channel data and learning supervised features to automatically extract features from multi-modal preoperative brain images of high-grade glioma patients to predict overall survival time.
Journal ArticleDOI

Multi-Channel 3D Deep Feature Learning for Survival Time Prediction of Brain Tumor Patients Using Multi-Modal Neuroimages

Dong Nie, +10 more
- 31 Jan 2019 - 
TL;DR: This study proposes a multi-channel architecture of 3D convolutional neural networks (CNNs) for deep learning upon those metric maps, from which high-level predictive features are extracted for each individual patch of these maps.
Book ChapterDOI

Outcome Prediction for Patient with High-Grade Gliomas from Brain Functional and Structural Networks

Luyan Liu, +5 more
TL;DR: A machine learning-based HGG prediction framework that can effectively extract valuable features from complex human brain connectome using network analysis tools, followed by a novel multi-stage feature selection strategy to single out good features while reducing feature redundancy is devised.
Journal ArticleDOI

Dynamic Joint Domain Adaptation Network for Motor Imagery Classification

Hong Xiaolin, +6 more
TL;DR: In this article, a dynamic joint domain adaptation network based on adversarial learning strategy was proposed to learn domain-invariant feature representation, and thus improve EEG classification performance in the target domain by leveraging useful information from the source session.