World's Best Scientists 2026 revealed!
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Computer Science
China
2026

D-Index & Metrics

Computer Science

D-Index
114
Citations
56951
World Ranking
192
National Ranking
24

Research.com Recognitions

  • 2026 - Research.com Computer Science in China Leader Award
  • 2025 - Research.com Computer Science in China Leader Award
  • 2023 - Research.com Computer Science in China Leader Award
  • 2022 - Research.com Computer Science in China Leader Award

Overview

Pheng-Ann Heng is affiliated with the Chinese University of Hong Kong in China. Their research work spans primarily across the fields of Computer Science and Medicine, with a focus on several specialized subfields.

The main areas of study include:

  • Computer Vision and Pattern Recognition
  • Artificial Intelligence
  • Radiology, Nuclear Medicine and Imaging
  • Surgery
  • Molecular Biology

Pheng-Ann Heng's published research extensively covers topics such as:

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

The frequent publication venues for their work include:

  • arXiv (Cornell University)
  • IEEE Transactions on Medical Imaging
  • Medical Image Analysis
  • Proceedings of the AAAI Conference on Artificial Intelligence
  • Nature Communications

Pheng-Ann Heng has collaborated regularly with several co-authors, highlighting ongoing research partnerships. Frequent co-authors are:

  • Qi Dou
  • Yueming Jin
  • Chi-Wing Fu
  • Guangyong Chen
  • Hao Chen

Notable recent publications include:

  • The Liver Tumor Segmentation Benchmark (LiTS), 2022, Medical Image Analysis
  • Transformation-Consistent Self-Ensembling Model for Semisupervised Medical Image Segmentation, 2020, IEEE Transactions on Neural Networks and Learning Systems
  • A global benchmark of algorithms for segmenting the left atrium from late gadolinium-enhanced cardiac magnetic resonance imaging, 2020, Medical Image Analysis
  • Unsupervised Bidirectional Cross-Modality Adaptation via Deeply Synergistic Image and Feature Alignment for Medical Image Segmentation, 2020, IEEE Transactions on Medical Imaging
  • A Survey on Generative Diffusion Models, 2024, IEEE Transactions on Knowledge and Data Engineering

Best Publications

  • Diagnostic Assessment of Deep Learning Algorithms for Detection of Lymph Node Metastases in Women With Breast Cancer.

    Babak Ehteshami Bejnordi;Mitko Veta;Paul Johannes van Diest;Bram van Ginneken

  • H-DenseUNet: Hybrid Densely Connected UNet for Liver and Tumor Segmentation From CT Volumes

    Xiaomeng Li;Hao Chen;Xiaojuan Qi;Qi Dou

  • Deep Learning Techniques for Automatic MRI Cardiac Multi-Structures Segmentation and Diagnosis: Is the Problem Solved?

    Olivier Bernard;Alain Lalande;Clement Zotti;Frederick Cervenansky

  • Validation, comparison, and combination of algorithms for automatic detection of pulmonary nodules in computed tomography images: The LUNA16 challenge.

    Arnaud Arindra Adiyoso Setio;Alberto Traverso;Thomas de Bel;Moira S.N. Berens

  • Automated Melanoma Recognition in Dermoscopy Images via Very Deep Residual Networks

    Lequan Yu;Hao Chen;Qi Dou;Jing Qin

  • The Liver Tumor Segmentation Benchmark (LiTS)

    Patrick Bilic;Patrick Ferdinand Christ;Eugene Vorontsov;Grzegorz Chlebus

  • Uncertainty-Aware Self-ensembling Model for Semi-supervised 3D Left Atrium Segmentation

    Lequan Yu;Shujun Wang;Xiaomeng Li;Chi Wing Fu

  • Gland segmentation in colon histology images: The GlaS challenge contest

    Korsuk Sirinukunwattana;Josien P.W. Pluim;Hao Chen;Xiaojuan Qi

  • VoxResNet: Deep voxelwise residual networks for brain segmentation from 3D MR images

    Hao Chen;Qi Dou;Lequan Yu;Jing Qin

  • Automatic Detection of Cerebral Microbleeds From MR Images via 3D Convolutional Neural Networks

    Qi Dou;Hao Chen;Lequan Yu;Lei Zhao

  • REFUGE Challenge: A unified framework for evaluating automated methods for glaucoma assessment from fundus photographs

    José Ignacio Orlando;Huazhu Fu;João Barbossa Breda;Karel van Keer

  • DCAN: Deep Contour-Aware Networks for Accurate Gland Segmentation

    Hao Chen;Xiaojuan Qi;Lequan Yu;Pheng-Ann Heng

  • PU-Net: Point Cloud Upsampling Network

    Lequan Yu;Xianzhi Li;Chi-Wing Fu;Daniel Cohen-Or

  • 3D deeply supervised network for automated segmentation of volumetric medical images.

    Qi Dou;Lequan Yu;Hao Chen;Yueming Jin

  • Multilevel Contextual 3-D CNNs for False Positive Reduction in Pulmonary Nodule Detection

    Qi Dou;Hao Chen;Lequan Yu;Jing Qin

  • A new method of feature fusion and its application in image recognition

    Quan-Sen Sun;Sheng-Gen Zeng;Yan Liu;Pheng-Ann Heng

  • R³Net: Recurrent Residual Refinement Network for Saliency Detection

    Zijun Deng;Xiaowei Hu;Lei Zhu;Lei Zhu;Xuemiao Xu

  • DCAN: Deep contour-aware networks for object instance segmentation from histology images

    Hao Chen;Xiaojuan Qi;Lequan Yu;Qi Dou

  • FedDG: Federated Domain Generalization on Medical Image Segmentation via Episodic Learning in Continuous Frequency Space

    Quande Liu;Cheng Chen;Jing Qin;Qi Dou

  • Transformation-Consistent Self-Ensembling Model for Semisupervised Medical Image Segmentation

    Xiaomeng Li;Lequan Yu;Hao Chen;Chi-Wing Fu

  • Standard Plane Localization in Fetal Ultrasound via Domain Transferred Deep Neural Networks

    Hao Chen;Dong Ni;Jing Qin;Shengli Li

Frequent Co-Authors

Hao Chen
Hao Chen Chinese University of Hong Kong
Tien-Tsin Wong
Tien-Tsin Wong Chinese University of Hong Kong
Qi Dou
Qi Dou Chinese University of Hong Kong
Jing Qin
Jing Qin Hong Kong Polytechnic University
Chi-Wing Fu
Chi-Wing Fu Chinese University of Hong Kong
Lequan Yu
Lequan Yu University of Hong Kong
Dong Ni
Dong Ni Shenzhen University
Kup-Sze Choi
Kup-Sze Choi Hong Kong Polytechnic University
Kwong-Sak Leung
Kwong-Sak Leung Chinese University of Hong Kong
Daniel Cohen-Or
Daniel Cohen-Or Tel Aviv University

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