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Fan Zhou

Researcher at University of Electronic Science and Technology of China

Publications -  143
Citations -  3524

Fan Zhou is an academic researcher from University of Electronic Science and Technology of China. The author has contributed to research in topics: Computer science & Graph (abstract data type). The author has an hindex of 16, co-authored 127 publications receiving 1846 citations. Previous affiliations of Fan Zhou include University of North Carolina at Chapel Hill & Shanghai University of Finance and Economics.

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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

Genome-wide association analysis of 19,629 individuals identifies variants influencing regional brain volumes and refines their genetic co-architecture with cognitive and mental health traits

Bingxin Zhao, +10 more
- 01 Nov 2019 - 
TL;DR: The study provides insight into the overlapping genetic architecture of brain volume measures and cognitive and mental health traits and advances the understanding of the pleiotropy and genetic co-architecture between brain volumes and other traits.
Proceedings ArticleDOI

Identifying human mobility via trajectory embeddings

Qiang Gao, +5 more
TL;DR: A Recurrent Neural Networks (RNN) based semi-supervised learning model, called TULER (TUL via Embedding and RNN) is proposed, which exploits the spatio-temporal data to capture the underlying semantics of user mobility patterns.
Proceedings ArticleDOI

DeepLink: A Deep Learning Approach for User Identity Linkage

Fan Zhou, +5 more
TL;DR: Inspired by the recent successes of deep learning in different tasks, especially in automatic feature extraction and representation, this work proposes a deep neural network based algorithm for UIL, called DeepLink, which outperforms the state-of-the-art methods in terms of both linking precision and identity-match ranking.
Proceedings ArticleDOI

Meta-GNN: On Few-shot Node Classification in Graph Meta-learning

Fan Zhou, +5 more
TL;DR: Meta-GNN as mentioned in this paper proposes a graph meta-learning framework to tackle the few-shot node classification problem in graph meta learning settings, which obtains the prior knowledge of classifiers by training on many similar few-shotted learning tasks and then classifies the nodes from new classes with only few labeled samples.