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 currently part of the MICCAI 2024 organization committee one of the two first ever Open Data chairs. To promote sharing of medical imaging data, he is organizing the first ever Open Data Event at MICCAI 2024, with a focus on underrepresented diseases and populations, with for this year special attention for African datasets. To facilitate sharing of data, he established the AFRICAI repository, which other resources can use to make data FAIR.
Martijn is one of the initiators and PIs 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.
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.
For a better overview on his projects, see his BIGR home page: 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 .
PhD ''Streamlined Quantitative Imaging Biomarker Development'' ("cum laude"), 2022
Erasmus Medical Center, Rotterdam, the Netherlands
MSc Applied Physics, 2016
Delft University of Technology, Delft, the Netherlands
BSc Applied Physics, 2013
Delft University of Technology, Delft, the Netherlands
*Not officially mentioned as co-applicant due to formalities. Reference of formal applicant available upon request.
*Not officially mentioned as co-applicant due to formalities. Reference of formal applicant available upon request.
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.
Radiomics uses quantitative medical imaging features and AI to create predictive models which can be used as biomarkers. In this thesis, we have developped an adaptive radiomics framework to automatically optimize the radiomics workflow per application and demonstrate its use to create biomarkers in eight different clinical applications.
We work on various clinical applications with different clinicians, and thus also various technical solutions within medical image analysis & AI. Hence, there are usually too many projects to describe here: Currently, we do not have any predefined projects available, but feel free to contact me for more information if you are interested in an internship / thesis! For an overview of some past projects, please see https://bigr.nl/open-projects/.
*With a joint appointment at the Radiology & Nuclear and Pathology departments, Martijn is heading the AI for Integrated Diagnostics (AIID) research line. The AIID group develops novel multimodal machine learning methods to develop quantitative biomarkers, focused on medical imaging and application in concology.
Extending the work of his PhD, Martijn’s research focused on generalization of radiomics biomarkers (conventional and deep learning based) using automated machine learning and meta-learning. He worked on a variety of clinical applications (e.g. sarcoma, liver cancer, colorectal cancer, bladder cancer, melanoma, cardiomyopathy, neuroendocrine tumors, CRPS).
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