Tel: 1-(480)738-2575

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

AMIA Doctoral Dissertation Award Nov 2022
Top 2% Scientists Worldwide, Stanford University Nov 2022
MICCAI Young Scientist Publication Impact Award Finalist Sep 2022
Elsevier-MedIA Best Paper Award Oct 2020
Graduate Fellowship, Arizona State University Mar 2020
MICCAI Young Scientist Award Oct 2019
MICCAI Best Presentation Award Finalist Oct 2019
Outstanding Graduate, Dalian University of Technology June 2016

Recent News

One paper was accepted to ISBI 2023 Jan 2023
One paper was accepted to ICLR 2023 Jan 2023
I gave a keynote talk at AMIA 2022 Annual Symposium Nov 2022
Two abstracts were accepted to MedNeurIPS, and one paper was accepted to NeurIPS HILL Oct 2022
One paper was accepted to WACV 2023 Oct 2022
My Ph.D. dissertation received the AMIA Doctoral Dissertation Award Sep 2022

Call for Papers

"Imaging Informatics: Computer-Aided Diagnosis" due on July 30, 2023
"Artificial Intelligence in Biomedical Image Processing" (IF=2.838) due on July 31, 2023
"Artificial Intelligence Applications for Cancer Diagnosis in Radiology" due on April 24, 2023
"Advances of Deep Learning in Medical Image Interpretation" (IF=3.847) due on March 20, 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

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

Medical Imaging Meets NeurIPS 2022

Synthetic Tumors Make AI Segment Tumors Better

Qixin Hu, Junfei Xiao, Yixiong Chen, Shuwen Sun, Jie-Neng Chen, Alan Yuille, Zongwei Zhou*

> View Publication > View Code > View Slides > View Poster > View Talk

Assembling Existing Labels from Public Datasets to Diagnose Novel Diseases: COVID-19 in Late 2019

Zengle Zhu, Mintong Kang, Alan Yuille, Zongwei Zhou*

> View Publication > View Code > View Slides > View Poster > View Talk

NeurIPS HILL 2022

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

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

arXiv Preprints & Abstracts

arXiv Preprints

CLIP-Driven Universal Model for Organ Segmentation and Tumor Detection

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

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

Du’Shu Forum/The 2nd Youth Academic Forum Nov 26, 2022
> View Slides
AMIA 2022 Annual Symposium Nov 7, 2022

Awards and Honors

AMIA Doctoral Dissertation Award Nov 2022

Top 2% Scientists Worldwide, Stanford University Nov 2022

MICCAI Young Scientist Publication Impact Award Finalist Sep 2022

Elsevier-MedIA Best Paper Award Oct 2020

Graduate Fellowship, Arizona State University Mar 2020

MICCAI Young Scientist Award Oct 2019

MICCAI Best Presentation Award Finalist Oct 2019

Outstanding Graduate, Dalian University of Technology June 2016

Recent News​

One paper was accepted to ISBI 2023 Jan 2023

One paper was accepted to ICLR 2023 Jan 2023

I gave a keynote talk at AMIA 2022 Annual Symposium Nov 2022

Two abstracts were accepted to MedNeurIPS, and one paper was accepted to NeurIPS HILL Oct 2022

One paper was accepted to WACV 2023 Oct 2022

My Ph.D. dissertation received the AMIA Doctoral Dissertation Award Sep 2022

Call for Papers​

"Imaging Informatics: Computer-Aided Diagnosis" due on July 30, 2023

"Artificial Intelligence in Biomedical Image Processing" (IF=2.838) due on July 31, 2023

"Artificial Intelligence Applications for Cancer Diagnosis in Radiology" due on April 24, 2023

"Advances of Deep Learning in Medical Image Interpretation" (IF=3.847) due on March 20, 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

​

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

​

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*

Journal Publications

Medical Image Analysis

Models Genesis

Zongwei Zhou, Vatsal Sodha, Jiaxuan Pang, Michael B. Gotway, Jianming Liang*

​

Active, Continual Fine Tuning of Convolutional Neural Networks for Reducing Annotation Efforts

Zongwei Zhou, Jae Shin, Suryakanth Gurudu, Michael B. Gotway, Jianming Liang*

​

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*

​

UNet++: Redesigning Skip Connections to Exploit Multi-Resolution Features in Image Segmentation

Zongwei Zhou, Md Mahfuzur Rahman Siddiquee, Nima Tajbakhsh, Jianming Liang*

​

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*

​

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*

​

Conference Publications

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*

Recent News

Call for Papers

"Advances of Deep Learning in Medical Image Interpretation" (IF=3.847) due on March 20, 2023

Thesis: Towards Annotation-Efficient Deep Learning for Computer-Aided Diagnosis
AMIA Doctoral Dissertation Award

Thesis: Medical image classification based on deep learning
Advisor: Dr. Hongkai Wang

Best Paper Award Honourable Mention
Junfei Xiao*, Lequan Yu, Zongwei Zhou, Yutong Bai, Lei Xing, Alan Yuille, Yuyin Zhou

Young Scientist Award
Young Scientist Publication Impact Award, Finalist

Jie Liu, Yixiao Zhang, Jie-Neng Chen, Junfei Xiao, Yongyi Lu, Yixuan Yuan, Alan Yuille, Yucheng Tang, Zongwei Zhou