World's Best Scientists 2026 revealed!

D-Index & Metrics

Computer Science

D-Index
64
Citations
27914
World Ranking
2525
National Ranking
342

Overview

Hao Chen is affiliated with the Chinese University of Hong Kong in China and has a research profile spanning Medicine and Computer Science, with a strong emphasis on Medical Imaging and Artificial Intelligence applications.

The scientist has contributed extensively to both main and subfields of study, including:

  • Medicine
  • Computer Science

Within these broad disciplines, Hao Chen's subfields of research include:

  • Computer Vision and Pattern Recognition
  • Artificial Intelligence
  • Radiology, Nuclear Medicine and Imaging
  • Surgery
  • Pulmonary and Respiratory Medicine

Research topics frequently addressed in Hao Chen's work are:

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

Research outputs include papers published in notable venues such as:

  • arXiv (Cornell University)
  • IEEE Transactions on Medical Imaging
  • SSRN Electronic Journal
  • Medical Image Analysis
  • IEEE Journal of Biomedical and Health Informatics

The following are representative recent publications by Hao Chen that reflect their research focus:

  • The Liver Tumor Segmentation Benchmark (LiTS), 2022, Medical Image Analysis
  • FCOS: A Simple and Strong Anchor-free Object Detector, 2020, IEEE Transactions on Pattern Analysis and Machine Intelligence
  • Transformation-Consistent Self-Ensembling Model for Semisupervised Medical Image Segmentation, 2020, IEEE Transactions on Neural Networks and Learning Systems
  • Unsupervised Bidirectional Cross-Modality Adaptation via Deeply Synergistic Image and Feature Alignment for Medical Image Segmentation, 2020, IEEE Transactions on Medical Imaging
  • Towards a new generation of artificial intelligence in China, 2020, Nature Machine Intelligence

Hao Chen has frequently collaborated with several researchers in their field, including:

  • Pheng-Ann Heng
  • Kwang-Ting Cheng
  • Luyang Luo
  • Chunhua Shen
  • Huangjing Lin

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

  • 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

  • MagNet: A Two-Pronged Defense against Adversarial Examples

    Dongyu Meng;Hao Chen

  • 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

  • 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

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

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

  • 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

  • From Detection of Individual Metastases to Classification of Lymph Node Status at the Patient Level: The CAMELYON17 Challenge

    Peter Bandi;Oscar Geessink;Quirine Manson;Marcory Van Dijk

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

    Hao Chen;Xiaojuan Qi;Lequan Yu;Qi Dou

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

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

  • A Multi-Organ Nucleus Segmentation Challenge

    Neeraj Kumar;Ruchika Verma;Deepak Anand;Yanning Zhou

  • Comparative Validation of Polyp Detection Methods in Video Colonoscopy: Results From the MICCAI 2015 Endoscopic Vision Challenge

    Jorge Bernal;Nima Tajkbaksh;Francisco Javier Sanchez;Bogdan J. Matuszewski

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

    Hao Chen;Dong Ni;Jing Qin;Shengli Li

  • Unsupervised Bidirectional Cross-Modality Adaptation via Deeply Synergistic Image and Feature Alignment for Medical Image Segmentation

    Cheng Chen;Qi Dou;Hao Chen;Jing Qin

  • Volumetric convnets with mixed residual connections for automated prostate segmentation from 3d MR images

    Lequan Yu;Xin Yang;Hao Chen;Jing Qin

  • Weakly Supervised Deep Learning for Whole Slide Lung Cancer Image Analysis

    Xi Wang;Hao Chen;Caixia Gan;Huangjing Lin

  • Transformation Consistent Self-ensembling Model for Semi-supervised Medical Image Segmentation

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

Frequent Co-Authors

Pheng-Ann Heng
Pheng-Ann Heng Chinese University of Hong Kong
Qi Dou
Qi Dou Chinese University of Hong Kong
Jing Qin
Jing Qin Hong Kong Polytechnic University
Lequan Yu
Lequan Yu University of Hong Kong
Chi-Wing Fu
Chi-Wing Fu Chinese University of Hong Kong
Xiaojuan Qi
Xiaojuan Qi University of Hong Kong
Nasir M. Rajpoot
Nasir M. Rajpoot University of Warwick
Hsiao Chang Chan
Hsiao Chang Chan Chinese University of Hong Kong
Dong Ni
Dong Ni Shenzhen University
Vincent Mok
Vincent Mok Chinese University of Hong Kong

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