Martijn P. A. Starmans
Assistant Professor Artificial Intelligence for Integrated Diagnostics (AIID) focused on Medical Imaging in Oncology
Erasmus Medical Center, Department of Radiology & Nuclear Medicine
Erasmus Medical Center, Department of Pathology
University of Barcelona
Biography
Martijn Starmans is an assistant professor at the Erasmus MC (Rotterdam, the Netherlands) leading the AI for Integrated Diagnostics (AIID) research line, with a dual appointment at the Biomedical Imaging Group Rotterdam (BIGR) (Dept. of Radiology & Nuclear Medicine) and PHANTOM group (Department of Pathology). His main research interest is the use of AI to improve the diagnostic work-up in oncology through integrated diagnostics, focussed on radiology (“radiomics”) and pathology (“pathomics”). Specifically, he develops multi-modal machine learning methods to simultanously co-learn from both modalities (“radiopathomics”), and automated machine learning and meta-learning methods to generalize these methods across clinial applications. He works on a variety of clinical applications (e.g. sarcoma, liver cancer, colorectal cancer, bladder cancer, melanoma, cardiology, neuroendocrine tumors).
Collaborations & Consortia
Martijn is involved in various large European projects. He is leader of the platform work package of the Horizon 2020 EuCanImage consortium: Towards a European cancer imaging platform for enhanced Artificial Intelligence in oncology, and also work package leader of the Horizon 2021 EOSC4Cancer consortium. Additionally, he is external advisor of RadioVal, and member of the AI4HI AI Development working group, and of EUCAIM. He has been a visiting researcher of the BCN-AIM lab of Prof. Karim Lekadir at the University of Barcelona in 2023 for 4 months.
Martijn is one of the initiators of the Sarcoma Artificial Intelligence (SAI) consortium (grant awarded), the Liver AI (LAI) consortium (grant awarded), and project lead of the Colorectal Liver Metastes AI (COLIMA) consortium (grant submitted). In these consortia, in total 51 clinical centers, companies, professional- and patient associations from 18 countries are united. He is also part of the Trustworthy AI for MRI ICAI LAB.
For a better overview on my projects, see https://bigr.nl/member/martijn/.
PhD Degree
Martijn obtained his PhD degree ‘‘cum laude’’ on February 1 2022 with his thesis titled Streamlined Quantitative Imaging Biomarker Development: Generalization of radiomics through automated machine learning. Following his passion to efficiently and automatically optimize routines, he developed an adaptive radiomics framework using automated machine learning, described in this paper. He collaborated with a large number of clinicians to develop radiomics biomarkers in a wide variety of clinical applications. His thesis was nominated for the Fredrik Philipsprijs 2023 for Best Dutch Radiology Thesis and made the top five.
Download my PhD Thesis.
Educational activities
Martijn has co-founded two courses in the MSc Technical Medicine, and is currently teaching two courses in the BSc MSC Technical Medicine and MSc Applied Physics of the TU Delft. Martijn also was part of the program committee and mentor in the first AFRICAI Summer School, and part of the first Rotterdam Radiology AI course organized by the Dutch Radiology Society. He enjoys working together with students and has so-far (co-)supervised 43 students in interships and thesis projects.
Download my (slightly outdated) resumé .
- Radiomics
- Pathomics
- RadioPathomics
- Multimodal machine learning
- AutoML
- Meta-Learning
- Oncology
- Soft-tissue tumors / sarcoma
- Liver cancer
- Colorectal liver metastases
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PhD ''Streamlined Quantitative Imaging Biomarker Development'' ("cum laude"), 2022
Erasmus Medical Center, Rotterdam, the Netherlands
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MSc Applied Physics, 2016
Delft University of Technology, Delft, the Netherlands
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BSc Applied Physics, 2013
Delft University of Technology, Delft, the Netherlands
Awards & Grants
- Role: Main applicant
- Topic: Radiology and pathology join forces through Artificial Intelligence for Integrated Diagnostics (AIID)
- Total funding: 2M Euro.
- Role: Participant
- Topic: PATH2XNAT: COVID 19 meets Pathomics meets XNAT
- Total funding: 280k Euro.
- Role: Co-applicant*
- Topic: The Liver Artificial Intelligence (LAI) consortium: a benchmark dataset and optimized machine learning methods for MRI-based diagnosis of solid appearing liver lesions
- Total funding: 1M Euro.
*Not officially mentioned as co-applicant due to formalities. Reference of formal applicant available upon request.
- Role: Co-applicant
- Topic: An artificial intelligence (AI)-based model for detection of incidental pulmonary embolism in chest CTs
- Total funding: 1M Euro.
- Role: Co-applicant*
- Topic: Automatic grading and phenotyping of soft-tissue tumors through machine learning to guide personalized cancer treatment
- Total funding: 400k Euro.
*Not officially mentioned as co-applicant due to formalities. Reference of formal applicant available upon request.
Featured Publications
The aim of the AI for Integrated Diagnostics (AIID) research line is to join forces of radiomics and pathomics to create trustworthy models to aid clinicians in decision making. Read more on the project page or the news article on the grant.
Recent Publications
Student Projects
I work on various clinical applications with many clinicians, and thus also various technical solutions. Hence, there are usually too many projects to describe here: feel free to contact me for more information if you are interested in an internship / thesis on radiomics, deep learning, and/or automated machine learning!
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Experience and Education
Extending the work of his PhD, Martijn works on generalization of radiomics and pathomics biomarkers (conventional and deep learning based) using automated machine learning and meta-learning. While working on a wide range of (oncologic) clinical applications, his main interests include soft tissue tumors and liver cancer. He is active in various technical and clinical networks, such as EuCanImage, EOSC4Cancer, the Sarcoma Artificial Intelligence (SAI) consortium, and the Liver Artificial Intelligence (LAI) consortium.
As of October 2023, Martijn has a permanent position shared between the Departments of Radiology & Nuclear Medicine and Pathology.
Responsibilities include:
- Grant applications
- Development of new methods
- Software implementation
- Teaching and continuous development of three courses in the BSc and MSc Technical Medicine
- Supervision of PhD students
Martijn will visit the group of Prof. Dr. Karim Lekadir to collaborate on AI for oncology and cardiac imaging within the context of the EuCanImage and euCanSHare consortia. While the main focus is on developing novel algorithms for (deep) radiomics, they will also work on federated learning, applying Martijn’s adaptive radiomics framework from his PhD to ``new’’ clinical applications, a second version of the FUTURE-AI guiding principles, and improving the clinical utility of (radiomics) AI tools.
Supervisors: Karim Lekadir
See extended description in the “about” section on this website.
Supervisors: Stefan Klein, Jan-Jaap Visser, Wiro Niessen
- Track: Imaging Physics
- Specialisation: Research & Development
- Thesis: Registration of 3D Breast Ultrasound Scans together with Erasmus Medical Center
- Honours track: Quantum Nanoscience
- Thesis: Activation cycles of stochastically blinking molecules in super-resolution microscopy
Skills
Contact
- m.starmans@erasmusmc.nl
- Doctor Molewaterplein 40, Rotterdam, 3015GD
- Department of Radiology & Nuclear Medicine, Room Na-2616
- DM Me