• [10/2022] One paper was accepted by TMI.
• [09/2022] One paper was accepted by NeurIPS 2022.
• [09/2022] One paper was accepted by TPAMI.
• [06/2022] Four papers were accepted by MICCAI 2022.
• [04/2022] Named on the World's First List of Top 150 Chinese Young Scholars in Artificial Intelligence.
• [04/2022] One paper was accepted by TMI.
• [03/2022] One paper was accepted by CVPR 2022.
• [02/2022] Two papers were accepted by MedIA.
• [01/2022] One paper was accepted by TMI.
• [12/2021] One paper was accepted by AAAI 2022.
• [08/2021] One paper about metal artifact reduction was accepted by PMB.
• [07/2021] We are organizing ICCV2021 workshop on Computer Vision for Automated Medical Diagnosis.
• [06/2021] Two papers were accepted by MICCAI 2021.
• [05/2021] One paper about multi-output consistency regularization was accepeted by TMI.
• [04/2021] One paper about self-supervised learning was accepted by TMI.
• [04/2021] Two papers were accepted by Medical Physics.
• [11/2020] One paper about annotation-efficient cardiac segmentation was accepted by TMI.
• [09/2020] Two papers were accepted by TMI.
• [07/2020] I have received the "CUHK Young Scholars Thesis Award 2019", the only awardee in the Faculty of Engineering.
• [07/2020] Three papers were accepted by ECCV 2020, TMI, and MedIA.
• [06/2020] Four papers were accepted by MICCAI 2020.
• [06/2020] One paper about multi-site learning was accepted by TMI.
• [05/2020] Two papers about semi-supervised learning for medical image analysis were accepted by TMI and TNNLS.

Experience

• Stanford University, Palo Alto, California, USA
  Nov. 2019 – Mar. 2021
  
  Postdoctoral Research Fellow
  Advisor: Prof. Lei Xing
  
• NVIDIA, deep learning for medical imaging research group, Bethesda, Maryland, USA
  Jul. 2018 – Oct. 2018
  
  Applied Research Intern
  Topic: Few-shot medical image segmentation
  
• Siemens Healthineers, Princeton, New Jersey, USA
  Mar. 2017 – Jul. 2017
  
  Research Intern
  Topic: Body landmark detection via deep reinforcement learning
  

Selected Publications [Google Scholar]

* indicates equal contribution; ^† indicates corresponding authorship.

2022

• Multi-Granularity Cross-modal Alignment for Generalized Medical Visual Representation Learning
  Fuying Wang, Yuyin Zhou, Shujun Wang, Varut Vardhanabhuti, Lequan Yu.
  Conference on Neural Information Processing Systems (NeurIPS), 2022.
  
• MCIBI++: Soft Mining Contextual Information Beyond Image for Semantic Segmentation
  Zhenchao Jin, Dongdong Yu, Zehuan Yuan, Lequan Yu.
  IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI), 2022.
  

[code]

• You Should Look at All Objects
  Zhenchao Jin, Dongdong Yu, Luchuan Song, Zehuan Yuan, Lequan Yu.
  European Conference on Computer Vision (ECCV), 2022.
  
• NestedFormer: Nested Modality-Aware Transformer for Brain Tumor Segmentation
  Zhaohu Xing, Lequan Yu, Liang Wan, Tong Han, Lei Zhu.
  Medical Image Computing and Computer Assisted Intervention (MICCAI), 2022.
  
• Joint Prediction of Meningioma Grade and Brain Invasion via Task-Aware Contrastive Learning
  Tianling Liu, Wennan Liu, Lequan Yu, Liang Wan, Tong Han, Lei Zhu.
  Medical Image Computing and Computer Assisted Intervention (MICCAI), 2022.
  
• Spatial-hierarchical Graph Neural Network with Dynamic Structure Learning for Histological Image Classification
  Wentai Hou, Helong Huang, Qiong Peng, Rongshan Yu, Lequan Yu, Liansheng Wang.
  Medical Image Computing and Computer Assisted Intervention (MICCAI), 2022.
  
• Reinforcement Learning Driven Intra-modal and Inter-modal Representation Learning for 3D Medical Image Classification
  Zhonghang Zhu, Liansheng Wang, Baptiste Magnier, Lei Zhu, Defu Zhang, Lequan Yu.
  Medical Image Computing and Computer Assisted Intervention (MICCAI), 2022.
  
• CD²-pFed: Cyclic Distillation-guided Channel Decoupling for Model Personalization in Federated Learning
  Yiqing Shen, Yuyin Zhou, Lequan Yu.
  IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2022.
  
• H²-MIL: Exploring Hierarchical Representation with Heterogeneous Multiple Instance Learning for Whole Slide Image Analysis
  Wentai Hou, Lequan Yu, Chengxuan Lin, Helong Huang, Rongshan Yu, Jing Qin, Liansheng Wang.
  AAAI Conference on Artificial Intelligence (AAAI), 2022.
  
• Lymph Node Metastasis Prediction from Whole Slide Images with Transformer-guided Multi-instance Learning and Knowledge Transfer
  Zhihua Wang, Lequan Yu, Xin Ding, Xuehong Liao, Liansheng Wang.
  IEEE Transactions on Medical Imaging (TMI), 2022.
  
• Novel-view X-ray Projection Synthesis through Geometry-integrated Deep Learning
  Liyue Shen, Lequan Yu, Wei Zhao, John Pauly, Lei Xing.
  Medical Image Analysis (MedIA), 2022.
  
• Towards Reliable Cardiac Image Segmentation: Assessing Image-level and Pixel-level Segmentation Quality via Self-reflective References
  Kang Li, Lequan Yu, Pheng-Ann Heng.
  Medical Image Analysis (MedIA), 2022.
  
• Robust Medical Image Classification from Noisy Labeled Data with Global and Local Representation Guided Co-training
  Cheng Xue, Lequan Yu, Pengfei Chen, Qi Dou, Pheng-Ann Heng.
  IEEE Transactions on Medical Imaging (TMI), 2022.
  

2021

• Metal Artifact Reduction in 2D CT Images with Self-supervised Cross-domain Learning
  Lequan Yu, Zhicheng Zhang, Xiaomeng Li, Hongyi Ren, Wei Zhao, Lei Xing.
  Physics in Medicine & Biology (PMB), 2021.
  
• TransCT: Dual-path Transformer for Low Dose Computed Tomography
  Zhicheng Zhang, Lequan Yu, Xiaokun Liang, Wei Zhao, Lei Xing.
  Medical Image Computing and Computer Assisted Intervention (MICCAI), 2021.
  

  [code]

• Selective Learning from External Data for CT Image Segmentation
  Youyi Song, Lequan Yu, Baiying Lei, Kup-Sze Choi, Jing Qin.
  Medical Image Computing and Computer Assisted Intervention (MICCAI), 2021.
  Student Travel Award
  

  [code]

• Deep Neural Network with Consistency Regularization of Multi-Output Channels for Improved Tumor Detection and Delineation
  Hyunseok Seo, Lequan Yu, Hongyi Ren, Xiaomeng Li, Liyue Shen, Lei Xing.
  IEEE Transactions on Medical Imaging (TMI), 2021.
  
• Rotation-oriented Collaborative Self-supervised Learning for Retinal Disease Diagnosis
  Xiaomeng Li, Xiaowei Hu, Xiaojuan Qi, Lequan Yu, Wei Zhao, Pheng-Ann Heng, Lei Xing.
  IEEE Transactions on Medical Imaging (TMI), 2021.
  

  [code]

• MR to ultrasound image registration with segmentation-based learning for HDR prostate brachytherapy
  Yizheng Chen, Lei Xing, Lequan Yu, Wu Liu, Benjamin P Fahimian, Thomas Niedermayr, Hilary Bagshaw, Mark K Buyyounouski, Bin Han.
  Medical Physics, 2021.
  
• Modularized Data-Driven Reconstruction Framework for Non-ideal Focal Spot Effect Elimination in Computed Tomography
  Zhicheng Zhang, Lequan Yu, Wei Zhao, Lei Xing.
  Medical Physics, 2021.
  
• Deep Sinogram Completion with Image Prior for Metal Artifact Reduction in CT Images
  Lequan Yu, Zhicheng Zhang, Xiaomeng Li, Lei Xing.
  IEEE Transactions on Medical Imaging (TMI), 2021.
  

2020

• Dual-Teacher++: Exploiting Intra-domain and Inter-domain Knowledge with Reliable Transfer for Cardiac Segmentation
  Kang Li, Shujun Wang, Lequan Yu^†, Pheng-Ann Heng.
  IEEE Transactions on Medical Imaging (TMI), 2020.
  
• Automatic intraprostatic lesion segmentation in multiparametric magnetic resonance images with proposed multiple branch UNet
  Yizheng Chen, Lei Xing, Lequan Yu, Hilary P. Bagshaw, Mark K. Buyyounouski, Bin Han.
  Medical Physics, 2020.
  
• Learning from Extrinsic and Intrinsic Supervisions for Domain Generalization
  Shujun Wang, Lequan Yu^†, Caizi Li, Chi-Wing Fu, Pheng-Ann Heng.
  European Conference on Computer Vision (ECCV), 2020.
  

  [code]

• DoFE: Domain-oriented Feature Embedding for Generalizable Fundus Image Segmentation on Unseen Datasets
  Shujun Wang, Lequan Yu^†, Kang Li, Xin Yang, Chi-Wing Fu, Pheng-Ann Heng.
  IEEE Transactions on Medical Imaging (TMI), 2020.
  

  [code]

• Deep Mining External Imperfect Data for Chest X-ray Diseases Screening
  Luyang Luo*, Lequan Yu*, Hao Chen, Quande Liu, Xi Wang, Jiaqi Xu, Pheng-Ann Heng.
  IEEE Transactions on Medical Imaging (TMI), 2020.
  
• Semi-supervised Medical Image Classication with Relation-driven Self-ensembling Model
  Quande Liu, Lequan Yu^†, Luyang Luo, Qi Dou, Pheng-Ann Heng.
  IEEE Transactions on Medical Imaging (TMI), 2020.
  

  [code]

• Towards Cross-modality Medical Image Segmentation with Online Mutual Knowledge Distillation
  Kang Li, Lequan Yu^†, Shujun Wang, and Pheng-Ann Heng.
  AAAI Conference on Artificial Intelligence (AAAI), 2020.
  
• Robust Medical Image Segmentation from Non-expert Annotations with Tri-network
  Tianwei Zhang*, Lequan Yu*, Na Hu, Su Lv, Shi Gu.
  Medical Image Computing and Computer Assisted Intervention (MICCAI), 2020.
  
• Dual-Teacher: Integrating Intra-domain and Inter-domain Teachers for Annotation-efficient Cardiac Segmentation
  Kang Li, Shujun Wang, Lequan Yu^†, Pheng-Ann Heng.
  Medical Image Computing and Computer Assisted Intervention (MICCAI), 2020.
  
• Difficulty-aware Meta-learning for Rare Disease Diagnosis
  Xiaomeng Li, Lequan Yu, Yueming Jin, Chi-Wing Fu, Lei Xing, Pheng-Ann Heng.
  Medical Image Computing and Computer Assisted Intervention (MICCAI), 2020.
  
• Unsupervised Detection of Distinctive Regions on 3D Shapes
  Xianzhi Li, Lequan Yu, Chi-Wing Fu, Daniel Cohen-Or, Pheng-Ann Heng.
  ACM Transactions on Graphics (ACM TOG), 2020.
  

  [code]

• Transformation-consistent Self-ensembling Model for Semi-supervised Medical Image Segmentation
  Xiaomeng Li, Lequan Yu, Hao Chen, Chi-Wing Fu, Lei Xing, Pheng-Ann Heng.
  IEEE Transaction on Neural Network and Learning System (TNNLS), 2020.
  

  [code]

2019

• Uncertainty-aware Self-ensembling Model for Semi-supervised 3D Left Atrium Segmentation
  Lequan Yu, Shujun Wang, Xiaomeng Li, Chi-Wing Fu, and Pheng-Ann Heng.
  Medical Image Computing and Computer Assisted Intervention (MICCAI), 2019.
  

  [code]

• CANet: Cross-disease Attention Network for Joint Diabetic Retinopathy and Diabetic Macular Edema Grading
  Xiaomeng Li, Xiaowei Hu, Lequan Yu, Lei Zhu, Chi-Wing Fu, and Pheng-Ann Heng.
  IEEE Transactions on Medical Imaging (TMI), 2019.
  

  [code]

• RMDL: Recalibrated multi-instance deep learning for whole slide gastric image classification
  Shujun Wang, Yaxi Zhu, Lequan Yu, Hao Chen, Huangjing Lin, Xiangbo Wan, Xinjuan Fan, and Pheng-Ann Heng.
  Medical Image Analysis (MedIA), 2019.
  
• Boundary and Entropy-driven Adversarial Learning for Fundus Image Segmentation
  Shujun Wang*, Lequan Yu*, Kang Li, Xin Yang, Chi-Wing Fu, and Pheng-Ann Heng.
  Medical Image Computing and Computer Assisted Intervention (MICCAI), 2019.
  

  [code]

• Patch-based Output Space Adversarial Learning for Joint Optic Disc and Cup Segmentation
  Shujun Wang, Lequan Yu, Xin Yang, Chi-Wing Fu, and Pheng-Ann Heng.
  IEEE Transactions on Medical Imaging (TMI), 2019.
  Ranked 1st place in REFUGE 2018 Optic Disc and Cup Segmentation Challenge
  

  [project]

2018

• PU-Net: Point Cloud Upsampling Network
  Lequan Yu, Xianzhi Li, Chi-Wing Fu, Daniel Cohen-Or, Pheng-Ann Heng.
  IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2018.
  

  [project] [code] [supp] [poster]

• EC-Net: an Edge-aware Point set Consolidation Network
  Lequan Yu, Xianzhi Li, Chi-Wing Fu, Daniel Cohen-Or, Pheng-Ann Heng.
  European Conference on Computer Vision (ECCV), 2018.
  

  [project] [code] [supp]

• Towards Automated Semantic Segmentation in Prenatal Volumetric Ultrasound
  Xin Yang, Lequan Yu, Shengli Li, Huaxuan Wen, Dandan Luo, Cheng Bian, Jing Qin, Dong Ni, Pheng-Ann Heng.
  IEEE Transactions on Medical Imaging (TMI), 2018.
  

  [project]

• Semi-supervised Skin Lesion Segmentation via Transformation Consistent Self-ensembling Model
  Xiaomeng Li, Lequan Yu, Hao Chen, Chi-Wing Fu, Pheng-Ann Heng.
  British Machine Vision Conference (BMVC), 2018.
  

Before 2018

• Volumetric ConvNets with Mixed Residual Connections for Automated Prostate Segmentation from 3D MR Images
  Lequan Yu, Xin Yang, Hao Chen, Jing Qin, Pheng-Ann Heng.
  AAAI Conference on Artificial Intelligence (AAAI), 2017. [Oral Presentation]
  Ranked 1st on Prostate MR Image Segmentation (PROMISE12) leaderboard, until Jan. 2018.
  

  [challenge website]

• Automatic 3D Cardiovascular MR Segmentation with Densely-Connected Volumetric ConvNets
  Lequan Yu, Jie-Zhi Cheng, Qi Dou, Xin Yang, Hao Chen, Jing Qin, Pheng-Ann Heng.
  Medical Image Computing and Computer Assisted Intervention (MICCAI), 2017.
  

  [project] [code]

• Automated Melanoma Recognition in Dermoscopy Images via Very Deep Residual Networks
  Lequan Yu, Hao Chen, Qi Dou, Jing Qin, Pheng-Ann Heng.
  IEEE Transactions on Medical Imaging (TMI), 2017.
  ESI Highly Cited Paper
  Ranked 1st place in Skin Lesion Analysis Towards Melanoma Detection Challenge (ISIC 2016)
  

  [project] [code] [challenge website]

• Integrating Online and Offline 3D Deep Learning for Automated Polyp Detection in Colonoscopy Videos
  Lequan Yu, Hao Chen, Qi Dou, Jing Qin, Pheng-Ann Heng.
  IEEE Journal of Biomedical and Health Informatics (JBHI), 2017.
  
• Towards Automatic Semantic Segmentation in Volumetric Ultrasound
  Xin Yang, Lequan Yu, Shengli Li, Xu Wang, Na Wang, Jing Qin, Dong Ni, Pheng-Ann Heng.
  Medical Image Computing and Computer Assisted Intervention (MICCAI), 2017. [Oral Presentation]
  

  [project]

• Fine-grained Recurrent Neural Networks for Automatic Prostate Segmentation in Ultrasound Images
  Xin Yang, Lequan Yu, Lingyun Wu, Yi Wang, Dong Ni, Jing Qin, Pheng-Ann Heng.
  AAAI Conference on Artificial Intelligence (AAAI), 2017. [Oral Presentation]
  
• 3D Deeply Supervised Network for Automated Segmentation of Volumetric Medical Images
  Qi Dou, Lequan Yu, Hao Chen, Yueming Jin, Xin Yang, Jing Qin, Pheng-Ann Heng.
  Medical Image Analysis (MedIA), 2017.
  ESI Highly Cited Paper
  MedIA-MICCAI'17 Best Paper Award
  

  [code]

• 3D FractalNet: Dense Volumetric Segmentation for Cardiovascular MRI Volumes
  Lequan Yu, Xin Yang, Jing Qin, Pheng-Ann Heng.
  MICCAI Workshop on Whole-Heart and Great Vessel Segmentation from 3D Cardiovascular MRI in Congenital Heart Disease, 2016.
  Ranked 1st place in Whole-Heart and Great Vessel Segmentation Challenge
  

  [challenge website]

• Automatic Detection of Cerebral Microbleeds from MR Images via 3D Convolutional Neural Networks
  Qi Dou*, Hao Chen*, Lequan Yu, Lei Zhao, Jing Qin, Defeng Wang, Vincent CT Mok, Lin Shi, Pheng-Ann Heng.
  IEEE Transactions on Medical Imaging (TMI), 2016.
  ESI Highly Cited Paper
  

  [project]

Students

Recent Talks & Presentations

• Medical Image Analysis and Reconstruction with Data-efficient Learning
  at VALSE 2022 workshop "医学数据分析中的深度学习方法", August 2022.
  at Beihang University, May 2022.
  at Zhejiang University, May 2022.
  at 海峡两岸暨港澳精准医学青年博士论坛, November 2021.
  at 中国医师协会第十五次放射医师年会, October 2021.
  at Nanjing University of Information Science and Technology, October 2021.
  at Department of Electrical and Electronic Engineering, HKU, September 2021.
  at MICS 2021, July 2021.
  at Data Science and Computational Statistics Seminar, University of Birmingham, February 2021.
  
• AI for Medical Imaging: Applications and Beyond
  at AI and Big Data Research for Health Improvement Symposium, Institute of Data Science, HKU, August 2022.
  at Mini-Symposium on Interdisciplinary Research, Faculty of Science, HKU, January 2022.
  at School of Biomedical Sciences, HKU, December 2021.
  
• The Applications of Transformer in Volumetric Segmentation and Low Dose CT
  at VALSE Webinar, October 2022.
  

Honors & Awards

Professional Activities

Teaching