Lequan Yu Postdoctoral Research Fellow 1070 Arastradero Road, Dept. of Radiation Oncology Stanford University Palo Alto, CA 94304 Email: lequany at stanford dot edu

Biography

I am a postdoctoral research fellow in Stanford University, working with Prof. Lei Xing. Before that, I obtained my Ph.D. degree in the Department of Computer Science and Engineering, The Chinese University of Hong Kong, supervised by Prof. Pheng-Ann Heng and Prof. Chi-Wing Fu in July 2019. Previously, I received the B.Eng degree from Department of Computer Science and Technology in Zhejiang University in 2015, under the supervision of Prof. Deng Cai.

My research lies at the intersection of artificial intelligence and medical image analysis. I am dedicated to designing data-efficient learning methods for biomedical image analysis and reconstruction. I also have expertise in deep learning for 3D vision. Recently, I am working on new learning algorithms while minimizing human supervision (self-supervised, semi-supervised and multi-modality learning) and medical image reconstruction.

If you would like to work with me, feel free to send me an email. Remote collaboration is also welcome!

News

Experience

• Stanford University, Palo Alto, California, USA
  Nov. 2019 – Present
  
  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.

2020

• 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), 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.
  
• 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

• 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]

• 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]

• 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

• 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]

• 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]

• 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]

Honors & Awards

Professional Activities

Teaching