Baoqiang Ma

Postdoc, Image Science Institute, University Medical Centre Utrecht

Joined

December 2022

Names

Baoqiang Ma (Preferred)

Emails

****@umcg.nl (Confirmed)

****@umcutrecht.nl (Confirmed)

Personal Links

Google Scholar

ORCID

Career & Education History

Postdoc

Image Science Institute, University Medical Centre Utrecht (umcutrecht.nl)

2025 – 2028

PhD student

Department of Radiation Oncology, University Medical Center Groningen (umcg.nl)

2021 – 2025

Advisors, Relations & Conflicts

No relations added

Expertise

AI interpretability

2025 – Present

Concept-based interpretability

2025 – Present

Explainable AI methods evaluation

2025 – Present

Radiotherapy modelling

2021 – Present

Head and neck cancer

2021 – Present

Tumor treatment decision system

2021 – Present

Medical image analysis

2018 – Present

Brain imaging analysis

2017 – 2021

Publications

Can Vision-Language Models Enable More Efficient Concept-Based Learning with Less Supervision for Interpretable Lung Nodule Diagnosis?
Baoqiang Ma, Djennifer K. Madzia-Madzou, Jin Ouyang, Kenneth G.A. Gilhuijs
- MIDL 2026 - Short Papers Poster
- Readers: Everyone
Comparation of CT/PET/MRI in deep learning models for outcome prediction in oropharyngeal cancer patients
Baoqiang Ma, Jiapan Guo, Lisanne van Dijk, Johannes A. Langendijk, Peter M.A. van Ooijen, Stefan Both, Nanna M. Sijtsema
- Crossref
- Readers: Everyone
2019 Integrating CT image features improves clinical prediction models for outcome in nasopharyngeal carcinoma patients treated with (chemo)radiation
Guanzhi Zhou, Baoqiang Ma, Yan Li, pei yang, Yingrui Shi, Arjen van der Schaaf, Lisanne V van Dijk, J.A. Langendijk, Nanna Maria Sijtsema
- Radiotherapy and Oncology
- Readers: Everyone
Comparation of CT/PET/MRI in deep learning models for outcome prediction in oropharyngeal cancer patients
Baoqiang Ma, Jiapan Guo, Lisanne V. van Dijk, Johannes A. Langendijk, Peter M.A. van Ooijen, Stefan Both, Nanna M. Sijtsema
- Crossref
- Readers: Everyone
Enhancing Prognostic Outcome Prediction in Oropharyngeal Cancer Patients Using Multimodality Imaging and Advanced AI Techniques
Baoqiang Ma
- University of Groningen
- Readers: Everyone
A paired dataset of multi-modal MRI at 3 Tesla and 7 Tesla with manual hippocampal subfield segmentations
Lei Chu, Baoqiang Ma, Xiaoxi Dong, Yirong He, Tongtong Che, Debin Zeng, Zihao Zhang, Shuyu Li
- Scientific Data
- Readers: Everyone
PET and CT based DenseNet outperforms advanced deep learning models for outcome prediction of oropharyngeal cancer
Baoqiang Ma, Jiapan Guo, Lisanne V. van Dijk, Johannes A. Langendijk, Peter M.A. van Ooijen, Stefan Both, Nanna M. Sijtsema
- Radiotherapy and Oncology
- Readers: Everyone
The prognostic value of pathologic lymph node imaging using deep learning-based outcome prediction in oropharyngeal cancer patients
Baoqiang Ma, Alessia De Biase, Jiapan Guo, Lisanne V. van Dijk, Johannes A. Langendijk, Stefan Both, Peter M.A. van Ooijen, Nanna M. Sijtsema
- Physics and imaging in radiation oncology
- Readers: Everyone
MRI-Based Head and Neck Tumor Segmentation Using nnU-Net with 15-Fold Cross-Validation Ensemble
Frank N. Mol, Luuk van der Hoek, Baoqiang Ma, Bharath Chowdhary Nagam, Nanna M. Sijtsema, Lisanne V. van Dijk, Kerstin Bunte, Rifka Vlijm, Peter M. A. van Ooijen
- Crossref
- Readers: Everyone
PET/CT based transformer model for multi-outcome prediction in oropharyngeal cancer
Baoqiang Ma, Jiapan Guo, Alessia De Biase, Lisanne V. van Dijk, Peter M.A. van Ooijen, Johannes A. Langendijk, Stefan Both, Nanna M. Sijtsema
- Radiotherapy and Oncology
- Readers: Everyone

View all 20 publications

Co-Authors

View all 46 co-authors