NewsI am going to join CUHK as an Assistant Professor at the Department of Computer Science & Engineering, and co-affiliated with the CUHK T Stone Robotics Institute, starting from Jan 2020.
I am now a post-doc at the Department of Computing, Imperial College London, supervised by Dr. Ben Glocker in BioMedIA. Before that, I obtained my Ph.D. degree in the Department of Computer Science and Engineering, The Chinese University of Hong Kong (CUHK), supervised by Prof. Pheng Ann Heng in July 2018. I received B. Eng. degree in Biomedical Engineering from Beihang University (BUAA) in Beijing, June 2014. I worked with Dr. Yan Xu for undergraduate research in MSRA.
Medical Image Analysis, Deep Learning, Machine Learning.
I focus on interdisciplinary researches at medical image analysis and artificial intelligence, for improving lesion detection, anatomical structure segmentation and quantification, cancer diagnosis and therapy, and surgical robotic perception. I have expertise in 3D deep learning for high-dimensional medical image computing.
Selected PublicationsFor a complete list, please check my google scholar.
Domain Generalization via Model-Agnostic Learning of Semantic Features.
Machine Learning with Multi-Site Imaging Data: An Empirical Study on the Impact of Scanner Effects.
Improving RetinaNet for CT Lesion Detection with Dense Masks from Weak RECIST Labels.
Incorporating Temporal Prior from Motion Flow for Instrument Segmentation in Minimally Invasive Surgery Video.
Robust Multimodal Brain Tumor Segmentation via Feature Disentanglement and Gated Fusion.
CIA-Net: Robust Nuclei Instance Segmentation with Contour-aware Information Aggregation.
Robust Learning at Noisy Labeled Medical Images: Applied to Skin Lesion Classification.
Synergistic Image and Feature Adaptation: Towards Cross-Modality Domain Adaptation for Medical Image Segmentation.
PnP-AdaNet: Plug-and-Play Adversarial Domain Adaptation Network at Unpaired Cross-modality Cardiac Segmentation.
Unsupervised Cross-Modality Domain Adaptation of ConvNets for Biomedical Image Segmentations
Semantic-Aware Generative Adversarial Nets for Unsupervised Domain Adaptation in Chest X-ray Segmentation.
SV-RCNet: Workflow Recognition from Surgical Videos Using Recurrent Convolutional Network.
3D Multi-scale FCN with Random Modality Voxel Dropout Learning for Intervertebral Disc Localization and
Segmentation from Multi-modality MR Images.
SFCN-OPI: Detection and Fine-grained Classification of Nuclei Using Sibling FCN with Objectness Prior Interaction.
Automated Pulmonary Nodule Detection via 3D ConvNets with Online Sample Filtering and
3D Deeply Supervised Network for Automated Segmentation of Volumetric Medical Images.
Qi Dou, Lequan Yu, Hao Chen, Yueming Jin, Xin Yang, Jing Qin, and Pheng Ann Heng.
VoxResNet: Deep Voxelwise Residual Networks for Brain Segmentation from 3D MR Images.
Hao Chen, Qi Dou, Lequan Yu, Jing Qin, and Pheng Ann Heng.
Multi-level Contextual 3D CNNs for False Positive Reduction in Pulmonary Nodule Detection.
Qi Dou, Hao Chen, Lequan Yu, Jing Qin, and Pheng Ann Heng.
3D Fully Convolutional Networks for Intervertebral Disc Localization and Segmentation.
Hao Chen*, Qi Dou*, Xi Wang, Jing Qin, Jack C. Y. Cheng and Pheng Ann Heng.
3D Deeply Supervised Network for Automatic Liver Segmentation from CT Volumes.
Automatic Detection of Cerebral Microbleeds from MR Images via 3D Convolutional Neural Networks.
Mitosis Detection in Breast Cancer Histology Images via Deep Cascaded Networks.
Automatic Fetal Ultrasound Standard Plane Detection Using Knowledge Transferred Recurrent Neural Networks.
Automatic Cerebral Microbleeds Detection from MR Images via Independent Subspace Analysis Based Hierarchical Features.
(* indicates equal contribution)
Automatic Lesion Detection with Three-dimensional Convolutional Neural Networks.
Deep Multilevel Contextual Networks for Biomedical Image Segmentation.
Unsupervised Domain Adaptation of ConvNets for Medical Image Segmentations via Adversarial Learning.
Deep Convolutional Networks for Automated Volumetric Cardiovascular Image Segmentation: From a Design Perspective.
Deep Cascaded Networks for Sparsely Distributed Object Detection from Medical Images.
TalksTalk on "Surgical Visual Perception Towards AI-Powered Context-Aware Operating Theaters"
at MICCAI'19 Workshop of OR 2.0 Context-Aware Operating Theaters, Oct 2019. [link]
at MediCIS, Université de Rennes 1, France, Nov 2019.
Talk on "Multimodal Learning from Unpaired Medical Images for Adaptation and Integration"
at UCL Medical Image Computing Summer School (MedICSS), UK, July 2019.
at UCL/ICL Bio-Imaging Symposium, UK, May 2019.
Talk on "Analyzing High Dimensional Medical Images with Deep Learning"
at Hamlyn Centre for Robotic Surgery, Imperial College London, UK, Feb 2019.
at Smart Robotics and Artificial Intelligence Workshop organized by Signate Life Sciences, HK, Dec 2018.
Talk on "Towards AI-Powered Healthcare: Automated Medical Image Computing via Deep Learning"
at Department of Computer Science, University of Birmingham， Nov 2019.
at Department of Computer Science, Sheffield University, May 2019.
at Department of Computer Science & Engineering, CUHK, Mar 2019.
at Department of Computer Science & Engineering, HKUST, Feb 2019.
at Hong Kong Instuition of Science Annual Meeting, HK, Dec 2018.
Talk on "Deep Learning for AI-Powered Medical Image Analysis in Radiology"
at Hong Kong College of Radiologists (HKCR) Annual Scientific Meeting, HK, November 2018.
Talk at Engineering Medical Innovation (EMedI) Summit 2018.
organized by CUHK Chow Yuk Ho Technology Center for Innovative Medicine, August 2018.
Talk on "Deep Learning for Medical Image Computing"
at Department of Computing, HKPolyU, HK, August 2018.
Talk on "Medical Image Computing via Deep Learning -- Detection and Segmentation"
at BioMedIA lab in Imperial College London, UK, July 2018.
at German Center for Neurodegenerative Diseases (DZNE), Germany, July 2018.
Talk on "Intelligent Medical Image Detection and Segmentaiton via 3D Deep Learning"
at AI in Healthcare Summit organized by ReWork, HK, June 2018. [link]
Talk on "Deep Learning for Medical Image Analysis: Algorithms and Applications"
at Department of Clinical Oncology at Queen Mary Hospital HKU, HK, April 2018.
Talk on "3D Convolutional Networks for Computer-aided Lesion Detection in Medical Images"
at A*Star Institute of High Performance Computing, Singapore, May 2017.
Talk on "Automated Brain Lesion Detection in MRI Scans with Hierarchical Features"
at Siemens Corporate Research, Princeton, US, June 2016.
PC of "Medical Imaging meets NeurIPS" 18-19
Reviewer of AISTATS'20, ICRA'20, AAAI'19-20, MICCAI'17-19, IJCAI'18-20, IROS'19
Medical Image Analysis (MedIA)
IEEE Transactions on Medical Imaging (TMI)
International Journal of Computer Vision (IJCV)
IEEE Transactions on Biomedical Engineering (TBME)
IEEE Transactions on Neural Networks and Learning Systems (TNNLS)
IEEE Transactions on Automation Science and Engineering (TASE)
IEEE Transactions on Cybernetics (CYB)
IEEE Robotics and Automation Letters (RA-L)
IEEE Journal of Biomedical and Health Informatics (JBHI)
IEEE Transactions on Emerging Topics in Computing (TETCSI)
International Journal of Computer Assisted Radiology and Surgery (IJCARS)
The Lancet Digital Health
Computerized Medical Imaging and Graphics
Journal of Magnetic Resonance Imaging
International Journal of Imaging Systems and Technology
SPIE Journal of Medical Imaging
SPIE Journal of Electronic Imaging
Informatics in Medicine Unlocked
Membership of IEEE, IEEE EMBS, MICCAI Society.
I like cycling, yoga, cooking, reading, travelling.
Understanding others is intelligent, understanding yourself is wisdom.
Last updated date: 31 Oct 2019.
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