Chongyu Qu

2023.3 - present, currently, I'm a research assistant at CCVL (Computational Cognition, Vision, and Learning) Lab at Johns Hopkins University, under the advisement of Prof. Alan L.Yuille and Dr. Zongwei Zhou.

2020.8 - 2022.5, I was a master's student at Department of Biomedical Engineering at Johns Hopkins University

2017.8 - 2020.5, I was an undergraduate student at The Ohio State University with a major in Biology

I am looking for Ph.D. positions in computer vision and medical image analysis in Fall 2024. If you are interested in my background and have some available positions, please let me know.

Email  /  CV  /  Scholar  /  Github

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Research Statement

My research interests lie in the intersection of computer vision and medical image analysis. My current focus is on developing a robust medical foundation model with adaptability to various downstream applications. The ultimate objective is to advance innovations in computer-aided diagnosis. This involves assisting experts in acquiring a more comprehensive understanding of the human body, delivering precise diagnoses, and enabling early detection.

Towards this goal, I have (1) curated a comprehensive abdominal dataset and introduced an active learning procedure to rapidly scale up annotations, applicable to both medical and natural imaging datasets. Building on this foundation, my more recent focus (2) involves extending annotations to the entire human body, facilitating tumor annotations, and designing customized AI model architectures and learning strategies to leverage these annotations. This effort is aimed at enhancing medical foundation models and enabling early detection tasks.

News

  • [Dec. 2023] Our challenge is accepted to ISBI 2024.
  • [Oct. 2023] One papers is accepted to NeurIPS 2023.
  • [Jul. 2023] Our abstract is accepted to RSNA 2023 (Oral Presentation).

Publications
AbdomenAtlas-8K: Annotating 8,000 CT Volumes for Multi-Organ Segmentation in Three Weeks

Chongyu Qu, Tiezheng Zhang, Hualin Qiao, Jie Liu, Yucheng Tang, Alan L. Yuille, Zongwei Zhou*
Conference on Neural Information Processing Systems (NeurIPS), 2023
[paper] [code] [bibtex]

This paper proposes an active learning procedure to expedite the annotation process for organ segmentation and creates the largest multi-organ dataset (by far) with the spleen, liver, kidneys, stomach, gallbladder, pancreas, aorta, and IVC annotated in 8,448 CT volumes, equating to 3.2 million slices.


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