Yaqian Chen

yaqian (dot) chen (at) duke (dot) edu

Duke University

Durham, NC, United States, 27703

About me: I am a Ph.D. student in Electrical and Computer Engineering at Duke University, working at the intersection of AI and Healthcare. I am fortunate to be advised by Prof. Maciej A. Mazurowski under Duke Spark Initiative. My research focuses on applying deep learning to medical image analysis, with particular emphasis on medical image segmentation and registration, foundation models, and clinically relevant AI methodologies.

I am interested in several research topics, including foundation models, self-supervised and semi-supervised learning, fine-tuning strategies, and clinically meaningful AI. My work spans medical image segmentation, registration, body composition analysis, breast density estimation, and risk prediction. I have developed registration networks for breast MRI that outperform existing models, established benchmarks for foundation model-based registration (awarded best paper at MICCAI workshop), and contributed to multiple medical segmentation models through foundation model fine-tuning.

Before starting my Ph.D. at Duke, I received my Master’s degree in Computer Science from Johns Hopkins University, where I worked in the Laboratory of Biomechanical and Image Guided Surgical Systems. Prior to that, I received my Bachelor’s degree in Computer Science and Engineering from The Chinese University of Hong Kong, Shenzhen, where I worked on simultaneous localization and mapping (SLAM) and robotics applications.

See my Google Scholar for a full list of my publications, with a few highlights shown below.

Feel free to contact me through email at yaqian.chen@duke.edu.

news

Aug 02, 2025	🎉 Thrilled to share that two of our papers on breast MRI have been accepted to the Deep-Breath Workshop at MICCAI 2025! 🧠 The first paper presents the most comprehensive breast MRI segmentation dataset to date, covering a wide range of anatomical structures. We hope this effort can lay the foundation for future research in breast composition analysis, risk stratification, and AI-driven screening tools. 🌀 The second paper focuses on generalizable breast MRI registration, addressing the challenge of aligning highly deformable anatomy across patients and imaging sessions. We demonstrate the power of foundation model features and training-free registration strategies to enable robust alignment without manual annotation. We believe these works together push toward more generalizable, scalable, and interpretable tools for breast MRI analysis. 🚀 Stay tuned — datasets, code, and more resources will be released soon. Big thanks to the Deep-Breath organizers and all our co-authors and collaborators!
Jun 29, 2025	🩺 Excited to share that our paper “Breast density in MRI: an AI-based quantification and relationship to assessment in mammography” has been accepted for publication in npj Breast Cancer! In this study, we developed an AI-driven pipeline to quantify breast density directly from MRI scans and explored how these MRI-derived metrics relate to traditional density assessments from mammography. Our work provides new insights into leveraging MRI for more comprehensive breast density evaluation. Huge thanks to all co-authors and collaborators — looking forward to seeing this work published soon!
Jun 25, 2025	🧠 Excited to share our latest work “MRI-CORE: A Foundation Model for Magnetic Resonance Imaging”, now available on arXiv! This project introduces a general-purpose MRI foundation model capable of handling diverse MRI sequences and anatomical regions, laying the groundwork for improved downstream tasks across medical imaging applications. 📄 Read the paper on arXiv Thank you to all collaborators for making this happen!
Mar 12, 2025	🦴 Thrilled to announce that “SegmentAnyBone: A Universal Model that Segments Any Bone at Any Location on MRI” has been accepted to Medical Image Analysis! This work presents the first generalized MRI bone segmentation model across anatomical regions and MRI sequences. Thank you to all collaborators!

selected publications

GuidedMorph: Two-Stage Deformable Registration for Breast MRI

Yaqian Chen, Hanxue Gu, Haoyu Dong, and 5 more authors

arXiv preprint arXiv:2505.13414, 2025

Abs Bib

@article{chen2025guidedmorph,
  title = {GuidedMorph: Two-Stage Deformable Registration for Breast MRI},
  author = {Chen, Yaqian and Gu, Hanxue and Dong, Haoyu and Li, Qihang and Chen, Yuwen and Konz, Nicholas and Li, Lin and Mazurowski, Maciej A},
  journal = {arXiv preprint arXiv:2505.13414},
  year = {2025},
  dimensions = {true},
  bibtex_show = true
}

Breast density in MRI: an AI-based quantification and relationship to assessment in mammography

Yaqian Chen, Lin Li, Hanxue Gu, and 5 more authors

npj Breast Cancer, 2025

Abs Bib

@article{chen2025breast,
  title = {Breast density in MRI: an AI-based quantification and relationship to assessment in mammography},
  author = {Chen, Yaqian and Li, Lin and Gu, Hanxue and Dong, Haoyu and Nguyen, Derek L and Kirk, Allan D and Mazurowski, Maciej A and Hwang, E Shelley},
  journal = {npj Breast Cancer},
  volume = {11},
  number = {1},
  pages = {115},
  year = {2025},
  publisher = {Nature Publishing Group UK London},
  dimensions = {true},
  bibtex_show = true
}

Automated muscle and fat segmentation in computed tomography for comprehensive body composition analysis

Yaqian Chen, Hanxue Gu, Yuwen Chen, and 7 more authors

arXiv preprint arXiv:2502.09779, 2025

Abs Bib

@article{chen2025automated,
  title = {Automated muscle and fat segmentation in computed tomography for comprehensive body composition analysis},
  author = {Chen, Yaqian and Gu, Hanxue and Chen, Yuwen and Yang, Jichen and Dong, Haoyu and Cao, Joseph Y and Camarena, Adrian and Mantyh, Christopher and Colglazier, Roy and Mazurowski, Maciej A},
  journal = {arXiv preprint arXiv:2502.09779},
  year = {2025},
  dimensions = {true},
  bibtex_show = true
}

Accelerating Volumetric Medical Image Annotation via Short-Long Memory SAM 2

Yuwen Chen, Zafer Yildiz, Qihang Li, and 5 more authors

arXiv preprint arXiv:2505.01854, 2025

Abs Bib

@article{chen2025accelerating,
  title = {Accelerating Volumetric Medical Image Annotation via Short-Long Memory SAM 2},
  author = {Chen, Yuwen and Yildiz, Zafer and Li, Qihang and Chen, Yaqian and Dong, Haoyu and Gu, Hanxue and Konz, Nicholas and Mazurowski, Maciej A},
  journal = {arXiv preprint arXiv:2505.01854},
  year = {2025},
  dimensions = {true},
  bibtex_show = true
}

Are Vision Foundation Models Ready for Out-of-the-Box Medical Image Registration?

Hanxue Gu, Yaqian Chen, Nicholas Konz, and 2 more authors

In Deep Breast Workshop on AI and Imaging for Diagnostic and Treatment Challenges in Breast Care, 2025

Abs Bib

@inproceedings{gu2025vision,
  title = {Are Vision Foundation Models Ready for Out-of-the-Box Medical Image Registration?},
  author = {Gu, Hanxue and Chen, Yaqian and Konz, Nicholas and Li, Qihang and Mazurowski, Maciej A},
  booktitle = {Deep Breast Workshop on AI and Imaging for Diagnostic and Treatment Challenges in Breast Care},
  pages = {101--112},
  year = {2025},
  organization = {Springer},
  dimensions = {true},
  bibtex_show = true
}

Monocular object SLAM using quadrics and landmark reference map for outdoor UAV applications

Pengtao Shao, Fan Mo, Yaqian Chen, and 2 more authors

In 2021 IEEE International Conference on Real-time Computing and Robotics (RCAR), 2021

Abs Bib

@inproceedings{shao2021monocular,
  title = {Monocular object SLAM using quadrics and landmark reference map for outdoor UAV applications},
  author = {Shao, Pengtao and Mo, Fan and Chen, Yaqian and Ding, Ning and Huang, Rui},
  booktitle = {2021 IEEE International Conference on Real-time Computing and Robotics (RCAR)},
  pages = {1195--1201},
  year = {2021},
  organization = {IEEE},
  dimensions = {true},
  bibtex_show = true
}

Segmentanybone: A universal model that segments any bone at any location on mri

Hanxue Gu, Roy Colglazier, Haoyu Dong, and 8 more authors

Medical Image Analysis, 2025

Abs Bib Code

@article{gu2025segmentanybone,
  title = {Segmentanybone: A universal model that segments any bone at any location on mri},
  author = {Gu, Hanxue and Colglazier, Roy and Dong, Haoyu and Zhang, Jikai and Chen, Yaqian and Yildiz, Zafer and Chen, Yuwen and Li, Lin and Yang, Jichen and Willhite, Jay and others},
  journal = {Medical Image Analysis},
  pages = {103469},
  year = {2025},
  publisher = {Elsevier},
  dimensions = {true},
  bibtex_show = true,
}

MRI-CORE: A Foundation Model for Magnetic Resonance Imaging

Haoyu Dong, Yuwen Chen, Hanxue Gu, and 4 more authors

arXiv preprint arXiv:2506.12186, 2025

Abs Bib

@article{dong2025mri,
  title = {MRI-CORE: A Foundation Model for Magnetic Resonance Imaging},
  author = {Dong, Haoyu and Chen, Yuwen and Gu, Hanxue and Konz, Nicholas and Chen, Yaqian and Li, Qihang and Mazurowski, Maciej A},
  journal = {arXiv preprint arXiv:2506.12186},
  year = {2025},
  dimensions = {true},
  bibtex_show = true
}

Improving Surgical Risk Prediction Through Integrating Automated Body Composition Analysis: a Retrospective Trial on Colectomy Surgery

Hanxue Gu, Yaqian Chen, Diego Schaps, and 5 more authors

arXiv preprint arXiv:2506.11996, 2025

Abs Bib

@article{gu2025improving,
  title = {Improving Surgical Risk Prediction Through Integrating Automated Body Composition Analysis: a Retrospective Trial on Colectomy Surgery},
  author = {Gu, Hanxue and Chen, Yaqian and Schaps, Diego and Woody, Regina and Colglazier, Roy and Mazurowski, Maciej A and Mantyh, Christopher and others},
  journal = {arXiv preprint arXiv:2506.11996},
  year = {2025},
  dimensions = {true},
  bibtex_show = true
}

Realtime robust shape estimation of deformable linear object

Jiaming Zhang, Zhaomeng Zhang, Yihao Liu, and 3 more authors

In 2024 IEEE International Conference on Robotics and Automation (ICRA), 2024

Abs Bib

@inproceedings{zhang2024realtime,
  title = {Realtime robust shape estimation of deformable linear object},
  author = {Zhang, Jiaming and Zhang, Zhaomeng and Liu, Yihao and Chen, Yaqian and Kheradmand, Amir and Armand, Mehran},
  booktitle = {2024 IEEE International Conference on Robotics and Automation (ICRA)},
  pages = {10734--10740},
  year = {2024},
  organization = {IEEE},
  dimensions = {true},
  bibtex_show = true
}