Han Liu - Siemens Healthineers

About Me

I'm a Research Scientist at Siemens Healthineers, building scalable AI for whole body oncology. My current work focuses on CT foundation models and automated report generation.

I envision a world where healthcare AI improves everyone's lives and cancers are caught far earlier. I'm committed to turning AI research into practical clinical tools with real impact, not just papers.

I bring practical expertise across the core medical imaging tasks: localization, detection, classification, segmentation, and synthesis. I've competed in many international medical imaging AI challenges and placed 1st place in several major ones, including CrossMoDA, PANORAMA, and VLM3D.

Education

Vanderbilt University

Aug. 2019 – May 2024

Ph.D. in Computer Science

Advised by Prof. Ipek Oguz and Prof. Benoit Dawant

IBM Fellowship, University Graduate Fellowship

Yale University

Aug. 2016 – Aug. 2017

M.S. in Biomedical Engineering

Advised by Prof. James Duncan

Rensselaer Polytechnic Institute

Aug. 2012 – May 2016

B.S. in Biomedical Engineering and Electrical Engineering

Advised by Prof. Ge Wang

Dean's List

Experience

Siemens Healthineers

Research Scientist. Team: Whole-body Oncology (WBO)

June 2024 – PRESENT

Scalable foundation model and smart adapter framework

Research Intern. Mentor: Dr. Zhoubing Xu

May 2022 – Dec. 2022

Universal 3D segmentation model via partial label learning

University of Pittsburgh Medical Center

Research Associate. Advisor: Prof. Jiantao Pu

Sep. 2017 – May 2019

Competitions

VLM3D Challenge - 1st Place

Team: Han Liu, Bogdan Georgescu, Yanbo Zhang, Gengyan Zhao, Youngjin Yoo, Eli Gibson, Sasa Grbic

Topic: Multi-abnormality Classification on Chest CT

Paper Code Award Post

PANORAMA Challenge - 1st Place

Team: Han Liu, Riqiang Gao, Sasa Grbic

Topic: Early Detection of PDAC on Abdominal Contrast-enhanced CT

Paper Code Award Post

CrossMoDA 2023 Challenge - 1st Place

Team: Han Liu, Yubo Fan, Zhoubing Xu, Benoit Dawant, Ipek Oguz

Topic: MRI Cross-modality Synthesis and Tumor Segmentation

Paper Code Award Post

MS-Multi-Spine 2025 Challenge - 1st Place

Team: Jiacheng Wang, Han Liu, Hao Li, Francesca Bagnato, Ipek Oguz

Topic: Multiple Sclerosis Spinal Cord Lesion Detection with Missing Modalities

Paper Code Award Post

Selected Publications

Please check Google Scholar for the full list of my publications.

IntraStyler: Intra-Domain Style Synthesis for Cross-Modality MRI Domain Adaptation

Han Liu, Yubo Fan, Hao Li, Dewei Hu, Daniel Moyer, Zhoubing Xu, Benoit M. Dawant, Ipek Oguz

MICCAI 2026 (Early Accept)

Paper Code Post

Revisiting 2D Foundation Models for Scalable 3D Medical Image Classification

Han Liu, Bogdan Georgescu, Yanbo Zhang, Youngjin Yoo, Michael Baumgartner, Riqiang Gao, Jianing Wang, Gengyan Zhao, Eli Gibson, Dorin Comaniciu, Sasa Grbic

CVPR 2026

🏆 1st place in VLM3D Challenge

Paper Code Poster Post

PanDx: AI-assisted Early Detection of Pancreatic Ductal Adenocarcinoma on Contrast-enhanced CT

Han Liu, Riqiang Gao, Eileen Krieg, Sasa Grbic

MICCAI AMAI 2025

🏆 1st place in PANORAMA Challenge

Paper Code

COSST: Multi-organ Segmentation with Partially Labeled Datasets Using Comprehensive Supervisions and Self-training

Han Liu, Zhoubing Xu, Riqiang Gao, Hao Li, Jianing Wang, Guillaume Chabin, Ipek Oguz, and Sasa Grbic

IEEE Transactions on Medical Imaging, 2024

Paper

COLosSAL: A Benchmark for Cold-start Active Learning for 3D Medical Image Segmentation

Han Liu, Hao Li, Xing Yao, Yubo Fan, Dewei Hu, Benoit Dawant, Vishwesh Nath, Zhoubing Xu, and Ipek Oguz

MICCAI 2023

Paper Code

Learning Site-specific Styles for Multi-institutional Unsupervised Cross-modality Domain Adaptation

Han Liu, Yubo Fan, Zhoubing Xu, Benoit Dawant, and Ipek Oguz

MICCAI BrainLes 2023

🏆 1st place in CrossMoDA 2023 Challenge

Paper Code

Evaluation of Synthetically Generated Computed Tomography for Use in Transcranial Focused Ultrasound Procedures

Han Liu*, Michelle Sigona*, Thomas Manuel, Li Min Chen, Benoit Dawant, Charles Caskey

Journal of Medical Imaging, 2023

Paper Code

ModDrop++: A Dynamic Filter Network with Intra-subject Co-training for Multiple Sclerosis Lesion Segmentation with Missing Modalities

Han Liu, Yubo Fan, Hao Li, Jiacheng Wang, Dewei Hu, Can Cui, Ho Hin Lee, Huahong Zhang, and Ipek Oguz

MICCAI 2022 (Early Accept)

Paper Code

SDFN: Segmentation-based Deep Fusion Network for Thoracic Disease Classification in Chest X-ray Images

Han Liu, Lei Wang, Yandong Nan, Faguang Jin, and Jiantao Pu

Computerized Medical Imaging and Graphics, 2019

Paper

Mentorship

Haoyu Dong Research Intern · June 2025 – Aug. 2025

In-context Learning for 3D Organ Segmentation in Whole-Body CT

Zhangxing Bian Research Intern · June 2025 – Dec. 2025

Scaling Novel Findings in Chest X-ray with Active Learning

Best Intern Poster Award

Book Chapters

Medical Image Segmentation Using Deep Learning

Han Liu*, Dewei Hu*, Hao Li*, Ipek Oguz

Machine Learning for Brain Disorders, Springer US, 2023

Chapter

Service

📝 Journal Reviewer

Medical Image Analysis (MedIA)
IEEE Transactions on Image Processing (TIP)
IEEE Journal of Biomedical and Health Informatics (JBHI)
IEEE Transactions on Neural Networks and Learning Systems (TNNLS)
The Journal of Machine Learning for Biomedical Imaging (MELBA)
Computers in Biology and Medicine (CIBM)
Computerized Medical Imaging and Graphics (CMIG)

🎓 Conference Reviewer

Medical Image Computing and Computer Assisted Interventions (MICCAI)
Medical Imaging with Deep Learning (MIDL)
International Symposium on Biomedical Imaging (ISBI)
Simulation and Synthesis in Medical Imaging
Medical Imaging Meets NeurIPS
Knowledge Discovery and Data Mining (KDD)

🎤 Guest Lectures

CS-8395 (2022 Fall): Open Source Programming for Medical Image Processing
CS-6357 (2023 Fall): Open Source Programming for Medical Image Analysis