Artificial Intelligence @ The Netherlands Cancer Institute
We design and research artificial intelligence algorithms to analyse oncological data
Latest news
Breast cancer risk prediction paper "OA-BreaCR" has been selected for oral presentation at MICCAI 2024
Recently, our paper, Ordinal Learning: Longitudinal Attention Alignment Model for Predicting Time to Future Breast Cancer Events from Mammograms has been selected at MICCAI2024 for oral presentation!
Read moreRadiology AI Lab at ESOI 2024!
We are proud to announce that our lab will be at ESOI - European Society of Oncologic Imaging 2024!
Read moreRadiology AI Lab at MICCAI 2024 Workshops!
We have four papers accepted at MICCAI - Medical Image Computing and Computed Assisted Intervention 2024 Workshops!
Read moreRadiology AI Lab at ESMO 2024!
We are proud to announce that our lab will be at ESMO - European Society for Medical Oncology Congress 2024!
Read moreKOFL travel fellowship awarded to Corentin Guérendel
Our PhD student Corentin Guérendel has won the KOFL travel fellowship from GROW - Research Institute for Oncology and Reproduction and Kankeronderzoekfonds Limburg.
Read moreOPTImaL clinical trial gets funded by KWF
The Dutch Cancer Society (KWF) dedicates 4.1 million euros for large-scale international research into forgoing chemotherapy in women with early-stage TNBC.
Read moreResearch
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
Read moreImage 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.
Read moreImmunology
Our AI research in immunology investigates interactions between T-cell receptors and peptides, and fundamental steps in the development of personalized cancer vaccines.
Read moreImmunotherapy 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.
Read moreNovel 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.
Read morePatient 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.
Read moreRadiogenomics
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.
Read moreReducing breast cancer overtreatment
An accurate risk assessment of breast cancers or their precursors is paramount to deciding on the appropriate treatment regime.
Read moreResponse 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.
Read moreTrustworthy 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.
Read moreVisual 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.
Read morePeople
The people working at the Netherlands Cancer Institute in Artificial Intelligence