


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. He received his Ph.D. in Biomedical Informatics at Arizona State University in 2021. His research focuses on developing novel methodologies to reduce the annotation efforts for computer-aided detection and diagnosis. Zongwei was the recipient of the AMIA Doctoral Dissertation Award in 2022, the Elsevier-MedIA Best Paper Award in 2020, and the MICCAI Young Scientist Award in 2019. In addition to two U.S. patents, Zongwei has published over twenty peer-reviewed journal/conference articles, two of which have been ranked among the most popular articles in IEEE TMI and the highest-cited article in EJNMMI Research. He was named the top 2% of Scientists released by Stanford University in 2022. Zongwei has been elected to the Guest Editor of Sensors and J. Imaging; Reviewer of IEEE TPAMI, MedIA, Information Fusion, IEEE TMI; and he was on the Program Committee for MICCAI in 2020-22; AAAI in 2020-23; CVPR in 2022-23; ICCV in 2021, MIDL in 2022. ORCiD: 0000-0002-3154-9851
Awards and Honors
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AMIA Doctoral Dissertation Award Nov 2022
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Top 2% Scientists Worldwide, Stanford University Nov 2022
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MICCAI Young Scientist Publication Impact Award Finalist Sep 2022
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Elsevier-MedIA Best Paper Award Oct 2020
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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
Recent News
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Two papers were provisionally accepted by MICCAI 2023 May 2023
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One paper was accepted to MIDL 2023 Mar 2023
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Three papers were accepted to CVPR 2023 Feb 2023
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Universal Model ranks first in the MSD Competition Feb 2023
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One paper was accepted to ISBI 2023; one paper was accepted to ICLR 2023 Jan 2023
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I gave a keynote talk at AMIA 2022 Annual Symposium Nov 2022
Call for Papers
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ICML Workshop on Interpretable Machine Learning in Healthcare due on May 30, 2023
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"Imaging Informatics: Computer-Aided Diagnosis" due on July 30, 2023
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"Artificial Intelligence in Biomedical Image Processing" (IF=2.838) due on July 31, 2023
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"Artificial Intelligence Applications for Cancer Diagnosis in Radiology" due on April 24, 2023
Education
Ph.D. Aug 2017-May 2021
Arizona State University
Department: Biomedical Informatics
Thesis: Towards Annotation-Efficient Deep Learning for Computer-Aided Diagnosis
AMIA Doctoral Dissertation Award
Advisors: Dr. Jianming Liang
> View Dissertation > View LaTeX > 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
Book Chapters
Intelligent Systems in Medicine and Health: The Role of AI
Interpreting Medical Images
Zongwei Zhou, Michael B. Gotway, Jianming Liang*
> View Book
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
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
MICCAI 2023
Learning without Forgetting for Continual Abdominal Multi-Organ and Tumor Segmentation
Yixiao Zhang, Xinyi Li, Huimiao Chen, Alan Yuille, Yaoyao Liu*, Zongwei Zhou*
> View Publication > View Code > View Slides > View Poster > View Talk
MetaLR: Meta-tuning of Learning Rates for Transfer Learning in Medical Imaging
Yixiong Chen, Li Liu, Jingxian Li, Hua Jiang, Zongwei Zhou
> View Publication > View Code > View Slides > View Poster > View Talk
MIDL 2023
Making Your First Choice: To Address Cold Start Problem in Vision Active Learning
Liangyu Chen, Yutong Bai, Siyu Huang, Yongyi Lu, Bihan Wen, Alan Yuille, Zongwei Zhou*
> View Publication > View Code > View Slides > View Poster > View Talk
CVPR 2023
Label-Free Liver Tumor Segmentation
Qixin Hu, Yixiong Chen, Junfei Xiao, Shuwen Sun, Jie-Neng Chen, Alan Yuille, Zongwei Zhou*
> View Publication > View Code > View Slides > View Poster > View Talk > View Alan's Talk
SQUID: Deep Feature In-Painting for Unsupervised Anomaly Detection
Tiange Xiang, Yixiao Zhang, Yongyi Lu, Alan Yuille, Chaoyi Zhang, Weidong Cai, Zongwei Zhou*
> View Publication > View Code > View Slides > View Poster > View Talk
Multispectral Video Semantic Segmentation: A Benchmark Dataset and Baseline
Wei Ji, Jingjing Li, Bian Cheng, Zongwei Zhou, Jiaying Zhao, Alan Yuille, Li Cheng*
> View Publication > View Code > View Slides > View Poster > View Talk
ISBI 2023
Label-Assemble: Leveraging Multiple Datasets with Partial Labels
Mintong Kang, Bowen Li, Zengle Zhu, Yongyi Lu, Elliot K. Fishman, Alan Yuille, Zongwei Zhou*
> View Publication > View Code > View Slides > View Poster > View Talk
ICLR 2023
Which Layer is Learning Faster? A Systematic Exploration of Layer-wise Convergence Rate for Deep Neural Networks
Yixiong Chen, Alan Yuille, Zongwei Zhou*
> View Publication > View Code > View Slides > View Poster > View Talk
WACV 2023
Delving into Masked Autoencoders for Multi-Label Thorax Disease Classification
Junfei Xiao, Yutong Bai, Alan Yuille, Zongwei Zhou*
> View Publication > View Code > View Slides > View Poster > View Talk
DART 2022
CateNorm: Categorical Normalization for Robust Medical Image Segmentation
Best Paper Award Honourable Mention
Junfei Xiao*, Lequan Yu, Zongwei Zhou, Yutong Bai, Lei Xing, Alan Yuille, Yuyin Zhou
> View Publication > View Code > View Slides > View Poster > View Talk
CVPR 2022
Learning from Temporal Gradient for Semi-supervised Action Recognition
Junfei Xiao, Longlong Jing, Lin Zhang, Ju He, Qi She, Zongwei Zhou, Alan Yuille, Yingwei Li*
> View Publication > View Code > View Slides > View Poster > View Talk
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 Slides > View Poster > View Talk
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
Young Scientist Publication Impact Award, Finalist
> 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 > View Talk
Preprints
Annotating 8,000 Abdominal CT Volumes for Multi-Organ Segmentation in Three Weeks
Chongyu Qu, Tiezheng Zhang, Hualin Qiao, Jie Liu, Yucheng Tang, Alan Yuille, Zongwei Zhou*
> View Publication > View Code > View Slides > View Poster > View Talk
CLIP-Driven Universal Model for Organ Segmentation and Tumor Detection
Rank first in Medical Segmentation Decathlon (MSD) Competition
Jie Liu, Yixiao Zhang, Jie-Neng Chen, Junfei Xiao, Yongyi Lu, Yixuan Yuan, Alan Yuille, Yucheng Tang*, Zongwei Zhou*
> View Publication > View Code > View Slides > View Poster > View Talk
US Patents
Methods, Systems, and Media for Discriminating and Generating Translated Images
Md Mahfuzur Rahman Siddiquee, Zongwei Zhou, Ruibin Feng, Nima Tajbakhsh, Jianming Liang
Granted on November 2, 2021; US Patent 11,164,021
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
Granted on November 2, 2021; US Patent 11,164,067
Invited Talks
Invited Talks
Scaling Datasets, Annotations, and Algorithms for Medical Image Analysis
Weill Cornell Radiology July 6, 2023
> View Blog > View Slides > View Talk
How to deal with publicly available datasets?
HIT Webinar Dec 16, 2022
> View Blog > View Slides > View Talk
How to develop a quality organization of doctoral dissertations and thesis defenses?
Seminar at University of Missouri-Columbia Dec 8, 2022
> View Blog > View Slides > View Talk
Towards Annotation-Efficient Deep Learning for Computer-Aided Diagnosis
CMLR at Peking University May 7, 2023
> View Slides
Du’Shu Forum/The 2nd Youth Academic Forum Nov 26, 2022
> View Slides
AMIA 2022 Annual Symposium Nov 7, 2022
> View Blog > View Slides > View Talk
Medical Image Computing Seminar (MICS) Aug 3, 2021
> View Blog > View Slides > View Talk
BMI Seminar Sep 4, 2020
> View Slides
Phoenix Symposium on Data Analytics in Healthcare Aug 13, 2020
> View Slides > View Talk
Data, Assemble: Towards Efficient Medical Image Analysis
MICCAI 2021 FLARE Challenge Keynote Oct 1, 2021
> View Blog > View Slides > View Talk
The Will of Computer Vision
VALSE Student Webinar Jan 28, 2021
> View Blog > View Slides > View Talk
Models Genesis: Generic Autodidactic Models for 3D Medical Image Analysis
AI Journal club Feb 29, 2020
> View Slides
Mila – Quebec Artificial Intelligence Institute Nov 11, 2019
> View Slides
AI Research Club Oct 24, 2019
> View Blog > View Slides > View Talk
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

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