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 medical imaging and artificial intelligence. 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]

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]

• 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.
  
• 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]

• MS-Net: Multi-Site Network for Improving Prostate Segmentation with Heterogeneous MRI Data
  Quande Liu, Qi Dou, Lequan Yu, and 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.
  

2019

• 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.
  
• 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]

• 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]

Before 2019

• 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.
  
• 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]
  

  [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
  

  [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.
  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]

• Automatic Detection of Cerebral Microbleeds via Deep Learning Based 3D Feature Representation
  Hao Chen*, Lequan Yu*, Qi Dou, Lin Shi, Vincent CT Mok, Pheng-Ann Heng.
  IEEE International Symposium on Biomedical Imaging (ISBI), 2015.
  

Honors & Awards

Professional Activities

Teaching