
Artificial Intelligence @ The Netherlands Cancer Institute
We design and research artificial intelligence algorithms to analyse oncological data
Latest news

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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We are at ESMO Sarcoma and Rare Cancers Congress 2025!
We are proud to announce that our lab will be at ESMO Sarcoma and Rare Cancers Congress 2025!
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Radiology AI Lab at ESGAR 2025!
We are proud to announce that our lab will be at ESGAR 2025!
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Radiology AI Lab at ECR 2025!
We are proud to announce that our lab will be at ECR - European Congress of Radiology 2025 in Vienna!
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Radiology AI Lab at BNAIC 2024!
We are proud to announce that, at the end of November, we joined (and helped organize) BNAIC 2024!
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Radiology AI Lab is a centre of excellence for Technical Medicine!
The Radiology AI Lab has now been awarded the premium status for the Technical Medicine programme.
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The research projects of the Netherlands Cancer Institute in Artificial Intelligence

Breast image analysis
We develop deep learning techniques to enhance the capabilities of breast imaging including digital breast tomosynthesis, mammography and dynamic contrast-enhanced breast MRI
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Image reconstruction
Medical imaging is the cornerstone of modern medicine. We build deep learning algorithms to reconstruct the measured machine data to an image of the patient anatomy.
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Immunology
Our AI research in immunology investigates interactions between T-cell receptors and peptides, and fundamental steps in the development of personalized cancer vaccines.
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Immunotherapy outcome prediction
Immunotherapy is a systemic cancer treatment that exploits the power of the body’s immune system to fight cancer cells by boosting the immune response. We develop algorithms to predict immunotherapy outcomes.
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Novel AI methodologies for Oncology
AI enables new diagnostic and treatment paradigms. However, its application to oncology brings many questions and challenges. Inspired by the oncological application, we research and develop new AI methodologies.
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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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Reducing breast cancer overtreatment
An accurate risk assessment of breast cancers or their precursors is paramount to deciding on the appropriate treatment regime.
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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 Netherlands Cancer Institute in Artificial Intelligence