About Me

I'm an AI/ML Scientist at Siemens Healthineers, developing scalable AI solutions for whole-body oncology applications. Currently, I'm building the next-generation CT scanner that automatically generates reports at a resident level.

I envision a world where healthcare AI can benefit everyone's life and cancers can be detected much earlier. I am committed to translating AI innovations into practical clinical solutions with real-world impact, not just papers.

As a practitioner, I have technical expertise in core medical imaging tasks including localization, detection, classification, segmentation, and synthesis. I have participated in many international AI competitions in medical imaging and ranked 1st place in several major challenges such as CrossMoDA, PANORAMA, and VLM3D.

Education

Vanderbilt

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

Yale University

Aug. 2016 - Aug. 2017

M.S. in Biomedical Engineering

Advised by Prof. James Duncan

RPI

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

Siemens Healthineers

June 2024 - PRESENT

AI/ML Scientist. Team: Whole-body Oncology (WBO)

  • Scalable foundation model and smart adapter framework

awardInnovation Excellence Award

Siemens

Siemens Healthineers

May 2022 - Dec. 2022

Research Intern. Mentor: Dr. Zhoubing Xu

  • Universal 3D segmentation model with partially labeled datasets paper
UPMC

University of Pittsburgh Medical Center

Sep. 2017 - May 2019

Research Associate. Advisor: Prof. Jiantao Pu

  • Thoracic disease classification on chest X-ray paper
  • Optic disc/cup segmentation on fundus images paper
  • Vasculature identification surrounding pulmonary nodules paper

Competitions

vlm3d

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

paperPaper codeCode awardAward postPost

panorama

PANORAMA Challenge - 1st Place

Team: Han Liu, Riqiang Gao, Sasa Grbic

Topic: Early Detection of Pancreatic Ductal Adenocarcinoma (PDAC) on Abdominal Contrast-enhanced CT

paperPaper codeCode awardAward postPost

crossmoda2023

CrossMoDA 2023 Challenge - 1st Place

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

Topic: MRI Cross-modality Synthesis and Tumor Segmentation

paperPaper codeCode awardAward postPost

msspine

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

paperPaper codeCode awardAward postPost

Selected Publications

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

revisting

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

Under submission (馃弳1st place in VLM3D Challenge)

paperPaper codeCode

pandx

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

Han Liu, Riqiang Gao, Eileen Krieg, Sasa Grbic

MICCAI AMAI 2025 (馃弳1st place in PANORAMA Challenge)

paperPaper codeCode slidesSlides

cosst

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

paperPaper

intrastyler

IntraStyler: Exemplar-based Style Synthesis for Cross-modality Domain Adaptation

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

Technical Report (Extension of 馃弳1st place solution of CrossMoDA 2023 Challenge)

paperPaper codeCode slidesSlides posterPoster

colossal

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

paperPaper codeCode slidesSlides posterPoster

crossmoda

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 (馃弳1st place in CrossMoDA 2023 Challenge)

paperPaper codeCode slidesSlides posterPoster

evaluation

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

paperPaper codeCode

moddrop++

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)

paperPaper codeCode slidesSlides posterPoster

sdfn

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

paperPaper

Mentorship

Haoyu Dong 路 Research Intern 路 June 2025 - Aug. 2025
Project: In-context Learning for 3D Organ Segmentation in Whole-Body CT paperPaper
Zhangxing Bian 路 Research Intern 路 June 2025 - Dec. 2025
Project: Scaling Novel Findings in Chest X-ray with Active Learning paperPaper 路 awardBest Intern Poster Award

Book Chapters

Machine Learning for Brain Disorders

Medical Image Segmentation Using Deep Learning

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

Machine Learning for Brain Disorders, Springer US, 2023

chapterChapter

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