Riqiang Gao

CV / Scholar / Github

I have focused on artificial intelligence (AI) technologies since 2015 when I have worked on face recognition for my Bachelor thesis. During my Master's period (2015.09-2018.07) at Tsinghua University (research-oriented, 3-year program), I have continued working on face recognition topics, where I have devoted myself to machine learning, especially deep learning.

I'm working on AI for Healthcare as seinor AI scientist and project manager at Siemens Healthineers. I have been trained at Vanderbilt University from 2018.8 - 2022.3 as a Ph.D. student, where I mainly focus on medical imaging and informatics (specifically, lung cancer risk estimation). As the first author, I have won RFW All-conference Best Student Paper Finalist twice (2020, 2021) in SPIE Medical Imaging, and C.F. Chen Best Paper at Vanderbilt University.

The following is some selected publications. Full list is in Google Scholar

Multi-Agent Reinforcement Learning Meets Leaf Sequencing in Radiotherapy

Riqiang Gao, Florin-Cristian Ghesu, Simon Arberet, Shahab Basiri, Esa Kuusela, Martin Kraus, Dorin Comaniciu, Ali Kamen

ICML (2024)

[PDF] [Light Poster] [Highlight by Senior Vice President] [Innovation Excellence Award]

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, Sasa Grbic

Transactions on Medical Imaging (2024)

[PDF]

Flexible-cm gan: Towards precise 3d dose prediction in radiotherapy

Riqiang Gao, Bin Lou, Zhoubing Xu, Dorin Comaniciu, Ali Kamen

IEEE CVPR (2023)

[PDF] [Supp. PDF] [Poster] [Highlight by Senior Vice President]

Reducing uncertainty in cancer risk estimation for patients with indeterminate pulmonary nodules using an integrated deep learning model

Riqiang Gao, Thomas Li, Yucheng Tang, Kaiwen Xu, Mirza Khan, Michael Kammer, Sanja L Antic, Stephen Deppen, Yuankai Huo, Thomas A Lasko, Kim L Sandler, Fabien Maldonado, Bennett A Landman

Computers in Biology and Medicine (2022)

[PDF] [Code]

You May Need both Good-GAN and Bad-GAN for Anomaly Detection

Riqiang Gao, Zhoubing Xu, Guillaume Chabin, Awais Mansoor, Florin-Cristian Ghesu, Bogdan Georgescu, Bennett A. Landman, Sasa Grbic

Technical Report: Submitted to ICLR 2022, but further revision was discontinued due to the method's performance being below the current state-of-the-art.

[PDF]

Lung Cancer Risk Estimation with Incomplete Data: A Joint Missing Imputation Perspective

Riqiang Gao, Yucheng Tang, Kaiwen Xu, Ho Hin Lee, Steve Deppen, Kim L. Sandler, Pierre P. Massion, Thomas Lasko, Yuankai Huo, Bennett A. Landman

International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI) (2021)

[PDF] [Code] [Early Accepted] [Travel Award] [Rank 1st in Area Chair Pool]

Pancreas CT Segmentation by Predictive Phenotyping

Yucheng Tang, Riqiang Gao, Hohin Lee, Qi Yang, Xin Yu, Yuyin Zhou, Shunxing Bao, Yuankai Huo, Jeffrey Spraggins, Jack Virostko, Zhoubing Xu, and Bennett A. Landman

International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI) (2021)

[PDF]

Cancer Risk Estimation Combining Lung Screening CT with Clinical Data Elements

Riqiang Gao, Yucheng Tang, Mirza S. Khan, Kaiwen Xu, Alexis B. Paulson, Shelbi Sullivan, Yuankai Huo, Stephen Deppen, Pierre P. Massion, Kim L. Sandler, Bennett A. Landman

Radiology: Artificial Intelligence (2021)

[PDF] [Code]

Deep Multi-path Network Integrating Incomplete Biomarker and Chest CT Data for Evaluating Lung Cancer Risk

Riqiang Gao, Yucheng Tang, Kaiwen Xu, Michael N. Kammer, Sanja L. Antic, Steve Deppen, Kim L. Sandler, Pierre P. Massion, Yuankai Huo, Bennett A. Landman

Medical Imaging, SPIE (2021)

[PDF] [Code] [RFW All-conferecne Best Paper Finalist]

High-resolution 3D abdominal segmentation with random patch network fusion

Yucheng Tang, Riqiang Gao, Ho Hin Lee, Shizhong Han, Yunqiang Chen, Dashan Gao, Vishwesh Nath, Camilo Bermudez, Michael R. Savona, Richard G. Abramsond, Shunxing Bao, Ilwoo Lyu, Yuankai Huo, Bennett A. Landman

Medical Image Analysis (2021)

[PDF]

Time-Distanced Gates in Long Short-Term Memory Networks

Riqiang Gao, Yucheng Tang, Kaiwen Xu, Yuankai Huo, Shunxing Bao, Sanja L. Antic, Emily S. Epstein, Steve Deppen, Alexis B. Paulson, Kim L. Sandler, Pierre P. Massion, Bennett A. Landman

Medical Image Analysis (2020)

[PDF] [Code] [C.F. Chen best paper]

Multi-path x-D Recurrent Neural Networks for Collaborative Image Classification

Riqiang Gao, Yuankai Huo, Shunxing Bao, Yucheng Tang, Sanja L. Antic, Emily S. Epstein, Steve Deppen, Alexis B. Paulson, Kim L. Sandler, Pierre P. Massion, Bennett A. Landman

Neurocomputing (2020)

[PDF] [Code]

Deep Multi-task Prediction of Lung Cancer and Cancer-free Progression from Censored Heterogenous Clinical Imaging

Riqiang Gao, Lingfeng Li, Yucheng Tang, Sanja L. Antic, Alexis B. Paulson, Yuankai Huo, Kim L. Sandler, Pierre P. Massion, Bennett A. Landman

Medical Imaging, SPIE (2020)

[PDF] [RFW All-conferecne Best Paper Finalist]

Internal-transfer Weighting of Multi-task Learning for Lung Cancer Detection

Yiyuan Yang, Riqiang Gao*, Yucheng Tang, Sanja L. Antic, Steve Deppen, Yuankai Huo, Kim L. Sandler, Pierre P. Massion, Bennett A. Landman (* denotes mentor)

Medical Imaging, SPIE (2020)

[PDF] [Honorable Mentioned Poster]

Distanced LSTM: Time-Distanced Gates in Long ShortTerm Memory Models for Lung Cancer Detection

Riqiang Gao, Yuankai Huo, Shunxing Bao, Yucheng Tang, Sanja L. Antic, Emily S. Epstein, Aneri B. Balar, Steve Deppen, Alexis B. Paulson, Kim L. Sandler, Pierre P. Massion, Bennett A. Landman

MICCAI-MLMI (oral) (2019)

[[PDF][Code]

Margin Loss: Making Faces More Separable

Riqiang Gao, Fuwei Yang, Wenming Yang and Qingmin Liao

IEEE Signal Processing Letters (2018)

[PDF]

Two-stage Patch-based Sparse Multi-value Descriptor for Face Recognition

Riqiang Gao, Wenming Yang, Xiaoling Hu and Qingmin Liao

IEEE VCIP (2017)

[[PDF]