Email: zzhou82@jh.edu
Tel: 1-(480)738-2575
URL: www.zongweiz.com
Office: 248 Malone Hall, Johns Hopkins University, Baltimore, MD
Overview: Zongwei Zhou is a postdoctoral researcher at Johns Hopkins University supervised by Bloomberg Distinguished Professor Alan Yuille. He received his Ph.D. in Biomedical Informatics at Arizona State University supervised by Dr. Jianming Liang. He has also spent time at Mayo Clinic, University of California, Berkeley, and Université de Montréal. Drawing upon the realms of biomedical informatics, computer vision, and deep learning, his research focuses on developing novel methodologies to minimize the annotation efforts for computer-aided diagnosis and medical imaging. In addition to 12 U.S. patents pending, Zongwei has published over 10 peer-reviewed journal/conference articles, two of which have received the MICCAI Young Scientist Award and Elsevier-MedIA Best Paper Award. Two of his journal publications have been ranked among the most popular articles in IEEE TMI and the highest-cited article in EJNMMI Research, respectively. Furthermore, Zongwei has been awarded as the co-PI of the Bridges AI program from XSEDE. Zongwei also plays an active role in the leading societies of the computer vision and medical imaging field. He serves as a reviewer of IEEE TPAMI, MedIA, IEEE TMI, etc. and he was on the program committee for MICCAI in 2020, 2021; AAAI in 2020, 2021; ICCV in 2021.
Education
Ph.D. Aug 2017-May 2021
Arizona State University
Department: Biomedical Informatics
Thesis: Towards Annotation-Efficient Deep Learning for Computer-Aided Diagnosis
Advisors: Dr. Jianming Liang
> View Dissertation > View Talk > View Slides > View Transcript
B.S. Sep 2012-Jul 2016
Dalian University of Technology
Department: Computer Science
Thesis: Medical image classification based on deep learning
Advisor: Dr. Hongkai Wang
> View Dissertation > View Slides
Experience
Postdoctoral Researcher June 2021-present
Johns Hopkins University
Group: Computational Cognition, Vision, and Learning (CCVL)
Projects: Detect signs of pancreatic cancer in CT scans earlier and with more accuracy than humans
Research Internship Jan 2018-July 2018
Centre Hospitalier de l’Université de Montréal
Group: Laboratoire clinique de traitement de l’image (LCTI)
Projects: Predictive model of colorectal cancer liver metastases response to chemotherapy
Joint collaboration: Imagia and MILA
Research Internship June 2017-Jul 2017
Mayo Clinic, Rochester MN
Group: Radiology Informatics Lab
Projects: Thyroid Ultrasound Imaging, Tumor Radiogenomics
Awards and Honors
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Elsevier-MedIA Best Paper Award Oct 2020
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University Graduate Fellowship, Arizona State University Mar 2020
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MICCAI Young Scientist Award Oct 2019
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MICCAI Best Presentation Award Finalist Oct 2019
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Outstanding Graduate, Dalian University of Technology June 2016
Journal Publications
Medical Image Analysis
Models Genesis
Zongwei Zhou, Vatsal Sodha, Jiaxuan Pang, Michael B. Gotway, Jianming Liang
MedIA Best Paper Award
> View Publication > View Code > View Slides
Active, Continual Fine Tuning of Convolutional Neural Networks for Reducing Annotation Efforts
Zongwei Zhou, Jae Shin, Suryakanth Gurudu, Michael B. Gotway, Jianming Liang
> View Publication > View Code > View Slides
Transactions on Medical Imaging
Transferable Visual Words: Exploiting the Semantics of Anatomical Patterns for Self-supervised Learning
Fatemeh Haghighi, Mohammad Reza Hosseinzadeh Taher, Zongwei Zhou, Michael Gotway, Jianming Liang
> View Publication > View Code
UNet++: Redesigning Skip Connections to Exploit Multi-Resolution Features in Image Segmentation
Zongwei Zhou, Md Mahfuzur Rahman Siddiquee, Nima Tajbakhsh, Jianming Liang
> View Publication > View Code
Journal of Digital Imaging
Integrating Active Learning and Transfer Learning for Carotid Intima-Media Thickness Video Interpretation
Zongwei Zhou, Jae Shin, Ruibin Feng, R. Todd Hurst, Christopher B. Kendall, Jianming Liang
> View Publication
Journal of Zhejiang University (Engineering Science)
Evaluation of Machine Learning Classifiers for Diagnosing Mediastinal Lymph Node Metastasis of Lung Cancer from PET/CT Images
Hongkai Wang, Zhonghua Chen, Zongwei Zhou, Yingci Li, Peiou Lu, Wenzhi Wang, Wanyu Liu, Lijuan Yu
> View Publication
EJNMMI Research
Comparison of Machine Learning Methods for Classifying Mediastinal Lymph Node Metastasis of Non-small Cell Lung Cancer from 18F-FDG PET/CT Images
Hongkai Wang, Zongwei Zhou, Yingci Li, Zhonghua Chen, Peiou Lu, Wenzhi Wang, Wanyu Liu, Lijuan Yu
> View Publication
Conference Publications
MLMI 2021
Seeking an Optimal Approach for Computer-Aided Pulmonary Embolism Detection
Nahid Ul Islam, Shiv Gehlot, Zongwei Zhou, Michael Gotway, Jianming Liang
> View Publication > View Code > View Slides > View Poster
DART 2020
Parts2Whole: Self-supervised Contrastive Learning via Reconstruction
Ruibin Feng, Zongwei Zhou, Michael Gotway, Jianming Liang
> View Publication > View Code > View Poster
MICCAI 2020
Learning Semantics-enriched Representation via Self-discovery, Self-classification, and Self-restoration
Fatemeh Haghighi, Mohammad Reza Hosseinzadeh Taher, Zongwei Zhou, Michael Gotway, Jianming Liang
> View Publication > View Code > View Slides > View Poster > View Talk
ICCV 2019
Learning Fixed Points in Generative Adversarial Networks: From Image-to-Image Translation to Disease Detection and Localization
Md Mahfuzur Rahman Siddiquee, Zongwei Zhou, Ruibin Feng, Nima Tajbakhsh, Michael Gotway, Yoshua Bengio, Jianming Liang
> View Publication > View Code > View Poster
MICCAI 2019
Models Genesis: Generic Autodidactic Models for 3D Medical Image Analysis
Zongwei Zhou, Vatsal Sodha, Md Mahfuzur Rahman Siddiquee, Ruibin Feng, Nima Tajbakhsh, Michael Gotway, Jianming Liang
Young Scientist Award
> View Publication > View Code > View Slides > View Poster > View Talk
DLMIA 2018
UNet++: A Nested U-Net Architecture for Medical Image Segmentation
Zongwei Zhou, Md Mahfuzur Rahman Siddiquee, Nima Tajbakhsh, Jianming Liang
> View Publication > View Code > View Slides > View Poster > View Talk
CVPR 2017
Fine-tuning Convolutional Neural Networks for Biomedical Image Analysis: Actively and Incrementally
Zongwei Zhou, Jae Shin, Lei Zhang, Suryakanth Gurudu, Michael Gotway, Jianming Liang
> View Publication > View Code > View Slides > View Poster
arXiv Preprints & Abstracts
arXiv Preprints
Data, Assemble: Leveraging Multiple Datasets with Heterogeneous and Partial Labels
Mintong Kang, Yongyi Lu, Alan Yuille, Zongwei Zhou*
> View Publication > View Code > View Slides > View Poster
Frequency of Rational Fractions on [0, 1]
Zongwei Zhou, Dawei Lu
> View Publication
US Patents
Methods, systems, and media for discriminating and generating translated images
Md Mahfuzur Rahman Siddiquee, Zongwei Zhou, Ruibin Feng, Nima Tajbakhsh, Jianming Liang
16/875,680, 11/19/2020
Systems, methods, and apparatuses for implementing a multi-resolution neural network for use with imaging intensive applications including medical imaging
Zongwei Zhou, Md Mahfuzur Rahman Siddiquee, Nima Tajbakhsh, Jianming Liang
16/556,130, 3/5/2020
Methods, systems, and media for selecting candidates for annotation for use in training classifiers
Zongwei Zhou, Jae Shin, Jianming Liang
16/397,990, 10/31/2019
Systems, methods, and/or media, for selecting candidates for annotation for use in training a classifier
Zongwei Zhou, Jae Shin, Jianming Liang
15/965,691, 11/1/2018
Invited Talks and Blogs
Invited Talks
Data, Assemble: Towards Efficient Medical Image Analysis
MICCAI 2021 FLARE Challenge Keynote Oct 1, 2021
> View Blog > View Slides > View Talk
Towards Annotation-Efficient Deep Learning for Computer-Aided Diagnosis
Medical Image Computing Seminar (MICS) Aug 3, 2021
> View Blog > View Slides > View Talk
The Will of Computer Vision
VALSE Student Webinar Jan 28, 2021
> View Blog > View Slides > View Talk
Computer-aided Diagnosis and Therapy in Medical Imaging
BMI Seminar Sep 4, 2020
> View Slides
Cost-Effective Computer-Aided Diagnosis of Lung Cancer in Chest Computed Tomography
Phoenix Symposium on Data Analytics in Healthcare Aug 13, 2020
> View Slides > View Talk
Models Genesis: Generic Autodidactic Models for 3D Medical Image Analysis
AI Journal club Feb 29, 2020
> View Slides
Models Genesis: Generic Autodidactic Models for 3D Medical Image Analysis
Mila – Quebec Artificial Intelligence Institute Nov 11, 2019
> View Slides
3D Transfer Learning in Medical Image Analysis
AI Research Club Oct 24, 2019
> View Blog > View Slides > View Talk
Models Genesis: Generic Autodidactic Models for 3D Medical Image Analysis
MICS Webinar Sep 24, 2019
> View Slides > View Talk
UNet++: A Nested U-Net Architecture for Medical Image Segmentation
AI Research Club Sep 18, 2018
> View Blog > View Slides > View Talk
How to Cut Annotation Cost in Biomedical Imaging
Centre Hospitalier de l’Université de Montréal May 22, 2018
> View Slides
Hot Posts
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Hometown
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Family
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