Yong Fan
Computer Vision and Pattern Recognition · NeuroImage · 2005–2020
A researcher in the Science corpus: 10 papers; trusts 47 researchers and is trusted by 14 researchers here, as inferred from the citation graph.
Subfields
- Computer Vision and Pattern Recognition — 57 citations into it; #14,962 of 21,575 by trust here, #8,831 by citations
- Cognitive Neuroscience — 51 citations into it; #10,474 of 10,852 by trust here, #3,544 by citations
- Radiology, Nuclear Medicine and Imaging — 47 citations into it
- Psychiatry and Mental health — 39 citations into it; #182 of 1,338 by trust here, #274 by citations
- Physiology — 28 citations into it; #308 of 2,276 by trust here, #743 by citations
- Artificial Intelligence — 27 citations into it; #16,748 of 35,345 by trust here, #11,750 by citations
- Neurology — 19 citations into it; #362 of 1,455 by trust here, #368 by citations
- Biomedical Engineering — 10 citations into it
Whom Yong Fan trusts overall
Their strongest outbound trust, strongest first, inferred from their citations.
- Christos Davatzikos
- Dzung L. Pham
- Leo Breiman
- Susan M. Resnick
- John Ashburner
- Dinggang Shen
- Catriona D. Good
- Karl J. Friston
- Jerry L Prince
- Zhiqiang Lao
Who trusts Yong Fan overall
- Daoqiang Zhang
- Christos Davatzikos
- Rémi Cuingnet
- Manhua Liu
- Dinggang Shen
- Claudia Plant
- Alan C. Evans
- Nematollah Kayhan Batmanghelich
- Esther E. Bron
- Emilie Gerardin
Papers from Yong Fan's perspective
Ordered by how strongly the researchers Yong Fan trusts endorse each paper. Same corpus, a different order for every researcher.
- ImageNet classification with deep convolutional neural networks
- Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
- ImageNet: A large-scale hierarchical image database
- Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
- Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
- ImageNet Large Scale Visual Recognition Challenge
- Dropout: a simple way to prevent neural networks from overfitting
- Densely Connected Convolutional Networks
- Visualizing and Understanding Convolutional Networks
- Gradient-based learning applied to document recognition
Their papers
- COMPARE: Classification of Morphological Patterns Using Adaptive Regional Elements (2007) — 390 citations
- Classifying spatial patterns of brain activity with machine learning methods: Application to lie detection (2005) — 452 citations
- Individual Variation in Functional Topography of Association Networks in Youth (2020)
- MRI signatures of brain age and disease over the lifespan based on a deep brain network and 14 468 individuals worldwide. (2020) — 356 citations
- Harmonization of large MRI datasets for the analysis of brain imaging patterns throughout the lifespan (2019) — 521 citations
- A deep learning model integrating FCNNs and CRFs for brain tumor segmentation (2017) — 724 citations
- Detection of prodromal Alzheimer's disease via pattern classification of magnetic resonance imaging. (2008) — 403 citations
- Baseline and longitudinal patterns of brain atrophy in MCI patients, and their use in prediction of short-term conversion to AD: Results from ADNI (2008) — 611 citations
- Structural and functional biomarkers of prodromal Alzheimer's disease: A high-dimensional pattern classification study (2008) — 317 citations
- Spatial patterns of brain atrophy in MCI patients, identified via high-dimensional pattern classification, predict subsequent cognitive decline (2008) — 530 citations