
Radiology AI Lab @ The Netherlands Cancer Institute
At the forefront of integrating AI in oncologic imaging, our mission is to navigate through the complexities of cancer diagnosis and treatment planning by developing AI algorithms to unlock new potentials in radiology.
Latest news

George Agrotis has been selected as fellow in the ER Yves Menu Review fellowship!
We are pleased to announce that George Agrotis has been selected as fellow in the prestigious European Radiology Yves Menu Review fellowship, by the European Society of Radiology.
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We have been awarded the Healthy Future-UvA Seed Grant!
The University of Amsterdam (UvA) has announced that they have awarded one of their Healthy Future Seed Grants 2024 to Stefano Trebeschi, one of our postdoctoral researchers.
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We are proud to share that Tianyu successfully defended his PhD thesis!
On the 16th of December, Tianyu Zhang successfully defended his PhD thesis at Maastricht University.
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The research projects of the Radiology AI Lab

Patient and Treatment Monitoring
Patient monitoring in cancer care is crucial to track the efficacy of anti-cancer treatments and overall patient condition, utilizing methods as follow-ups and response assessments. Our goal is to leverage AI methods to propose optimized treatment strategies, objective progression definition and diagnostic precision.
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Radiogenomics
Radiogenomics brings together multiple disciplines. Our goal is to study the impact of somatic mutations on the morphology of the tumour, to identify radiomic signatures predictive of clinically relevant molecular profiles, and to explore MR techniques to predict tumour microenvironment.
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Response or Recur
Pre-treatment imaging has the power to give insights into response to therapy before it is given. Our goal is to leverage advanced machine learning combining imaging modalities to craft more personalized treatment plans. Currently these algorithms focus on prostate, rectal and oesophageal cancer, by using self-supervised and semi-supervised learning, image-to-image translation and multimodal AI.
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Trustworthy AI
It is extremely common that Deep Learning algorithm performs well in one medical dataset but is unable to generalize to a different dataset. Our goal is the development of robust algorithms, techniques to share data and understanding of AI decisions, by focusing on generalizability, privacy and explainable AI.
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Visual Foundational Models
Image encoders are often trained for project-specific tasks, failing to capture an optimal representation of the medical scan. Our goal is to develop powerful encoders capable of generating high-quality embeddings, and to subsequently use them for clinical-use cases, such as automatic radiology report generation or promptable segmentation via textual queries.
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The people working at the Radiology AI Lab