World's Best Scientists 2026 revealed!
Yefeng Zheng

Yefeng Zheng

D-Index & Metrics

Computer Science

D-Index
70
Citations
20385
World Ranking
1867
National Ranking
255

Research.com Recognitions

  • 2018 - Fellow of the Indian National Academy of Engineering (INAE)

Overview

Yefeng Zheng is affiliated with Tencent in China and has an extensive research portfolio encompassing computer science and medicine. Their work primarily focuses on artificial intelligence, computer vision, and biomedical imaging, with significant contributions in subfields such as artificial intelligence, computer vision and pattern recognition, radiology, nuclear medicine and imaging, biomedical engineering, and molecular biology.

Their research covers diverse topics, including:

  • Advanced Neural Network Applications
  • Topic Modeling
  • Domain Adaptation and Few-Shot Learning
  • Radiomics and Machine Learning in Medical Imaging
  • AI in cancer detection
  • COVID-19 diagnosis using AI
  • Medical Image Segmentation Techniques

Zheng has published extensively in venues like arXiv (Cornell University), IEEE Transactions on Medical Imaging, Medical Image Analysis, Proceedings of the AAAI Conference on Artificial Intelligence, and Pattern Recognition. These platforms reflect a consistent engagement with leading journals and conferences in artificial intelligence and medical imaging.

Frequent collaborators include:

  • Kai Ma (118 coauthored works)
  • Yuexiang Li (90 coauthored works)
  • Dong Wei (49 coauthored works)
  • Yawen Huang (45 coauthored works)
  • Donghuan Lu (44 coauthored works)

The scientist's recent papers demonstrate collaborations across domains and address key challenges in medical imaging and AI implementation:

  • The Medical Segmentation Decathlon, 2022, Nature Communications
  • A global benchmark of algorithms for segmenting the left atrium from late gadolinium-enhanced cardiac magnetic resonance imaging, 2020, Medical Image Analysis
  • Morphological diversity of single neurons in molecularly defined cell types, 2021, Nature
  • Deep Representation-Based Domain Adaptation for Nonstationary EEG Classification, 2020, IEEE Transactions on Neural Networks and Learning Systems
  • Rubik's Cube+: A self-supervised feature learning framework for 3D medical image analysis, 2020, Medical Image Analysis

In addition to journal publications, Zheng has contributed to book chapters published by Springer Science+Business Media. These works primarily focus on medical image computing and computer-assisted intervention, with multiple contributions in the Medical Image Computing and Computer Assisted Intervention - MICCAI 2021 book series.

Recognition for their scientific contributions includes being named a Fellow of the Indian National Academy of Engineering (INAE) in 2018.

Best Publications

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

    Spyridon Bakas;Mauricio Reyes;Andras Jakab;Stefan Bauer

  • The Medical Segmentation Decathlon

    Michela Antonelli;Annika Reinke;Spyridon Bakas;Keyvan Farahani

  • Four-Chamber Heart Modeling and Automatic Segmentation for 3-D Cardiac CT Volumes Using Marginal Space Learning and Steerable Features

    Yefeng Zheng;A. Barbu;B. Georgescu;M. Scheuering

  • Translating and Segmenting Multimodal Medical Volumes with Cycle- and Shape-Consistency Generative Adversarial Network

    Zizhao Zhang;Lin Yang;Yefeng Zheng

  • A global benchmark of algorithms for segmenting the left atrium from late gadolinium-enhanced cardiac magnetic resonance imaging.

    Zhaohan Xiong;Qing Xia;Zhiqiang Hu;Ning Huang

  • Robust point matching for nonrigid shapes by preserving local neighborhood structures

    Yefeng Zheng;D. Doermann

  • Morphological diversity of single neurons in molecularly defined cell types

    Hanchuan Peng;Hanchuan Peng;Peng Xie;Lijuan Liu;Xiuli Kuang

  • Med3D: Transfer Learning for 3D Medical Image Analysis

    Sihong Chen;Kai Ma;Yefeng Zheng

  • Combo loss: Handling input and output imbalance in multi-organ segmentation

    Saeid Asgari Taghanaki;Saeid Asgari Taghanaki;Yefeng Zheng;S. Kevin Zhou;Bogdan Georgescu

  • Multi-Scale Deep Reinforcement Learning for Real-Time 3D-Landmark Detection in CT Scans

    Florin-Cristian Ghesu;Bogdan Georgescu;Yefeng Zheng;Sasa Grbic

  • Inconsistency-aware Uncertainty Estimation for Semi-supervised Medical Image Segmentation

    Yinghuan Shi;Jian Zhang;Tong Ling;Jiwen Lu

  • Script-Independent Text Line Segmentation in Freestyle Handwritten Documents

    Yi Li;Yefeng Zheng;D. Doermann;S. Jaeger

  • Machine printed text and handwriting identification in noisy document images

    Yefeng Zheng;Huiping Li;D. Doermann

  • Deep Representation-Based Domain Adaptation for Nonstationary EEG Classification

    Unknown

  • Calibrated RGB-D Salient Object Detection

    Wei Ji;Jingjing Li;Shuang Yu;Miao Zhang

  • X2CT-GAN: Reconstructing CT From Biplanar X-Rays With Generative Adversarial Networks

    Xingde Ying;Heng Guo;Kai Ma;Jian Wu

  • Fast Automatic Heart Chamber Segmentation from 3D CT Data Using Marginal Space Learning and Steerable Features

    Yefeng Zheng;A. Barbu;B. Georgescu;M. Scheuering

  • PRGC: Potential Relation and Global Correspondence Based Joint Relational Triple Extraction

    Hengyi Zheng;Rui Wen;Xi Chen;Yifan Yang

  • Deep similarity learning for multimodal medical images

    Xi Cheng;Li Zhang;Yefeng Zheng

  • Benchmark for Algorithms Segmenting the Left Atrium From 3D CT and MRI Datasets

    Catalina Tobon-Gomez;Arjan J. Geers;Jochen Peters;Jurgen Weese

  • 3D Deep Learning for Efficient and Robust Landmark Detection in Volumetric Data

    Yefeng Zheng;David Liu;Bogdan Georgescu;Hien Nguyen

  • Hierarchical, learning-based automatic liver segmentation

    Haibin Ling;S.K. Zhou;Yefeng Zheng;B. Georgescu

  • Deep Learning and Convolutional Neural Networks for Medical Image Computing

    Le Lu;Yefeng Zheng;Gustavo Carneiro;Lin Yang

  • Method and system for anatomical object detection using marginal space deep neural networks

    Bogdan Georgescu;Yefeng Zheng;Hien Nguyen;Vivek Kumar Singh

  • Four-Chamber Heart Modeling and Automatic Segmentation for 3D Cardiac CT Volumes

    Yefeng Zheng;Bogdan Georgescu;Adrian Barbu;Michael Scheuering

Frequent Co-Authors

Dorin Comaniciu
Dorin Comaniciu Siemens (United States)
Bogdan Georgescu
Bogdan Georgescu Princeton University
S. Kevin Zhou
S. Kevin Zhou University of Science and Technology of China
David Doermann
David Doermann University at Buffalo, State University of New York
Joachim Hornegger
Joachim Hornegger University of Erlangen-Nuremberg
Linlin Shen
Linlin Shen Shenzhen University
Lin Yang
Lin Yang University of Florida
Andreas Maier
Andreas Maier University of Erlangen-Nuremberg
Deyu Meng
Deyu Meng Xi'an Jiaotong University
Pheng-Ann Heng
Pheng-Ann Heng Chinese University of Hong Kong

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