AI for Oncology Lab @ Netherlands Cancer Institute
The mission of the AI for Oncology Lab is to develop artificial intelligence innovations for the improvement of cancer diagnostics and therapy.
New publication about breast cancer primary treatment response assessment and prediction
Primary systemic therapy (PST) is the treatment of choice in patients with locally advanced breast cancer and is nowadays also often used in patients with early-stage breast cancer. In our review, we critically discuss the literature on AI-based PST response prediction.
New preprint about Constrained Empirical Risk Minimization
Our latest research provides an innovative solution to the challenge of enforcing constraints on deep neural networks. We reframe the constraints on a network as an ordinary risk minimization problem on a Riemannian manifold.
STAPLER: a language model to predict TCR–pMHC reactivity
Our latest paper introduces STAPLER, a cutting-edge language model that significantly enhances TCR-pMHC reactivity prediction, outperforming previous models in the field.
A deeper understanding of TCR-pMHC interactions is key to unlocking the potential of personalized immunotherapies and expanding our knowledge of the immune system.
GPU cluster expanded with gaia and galileo
We have added another 8xA6000 server (galileo) and a CPU server (gaia) to our Kosmos cluster. Kosmos now consists out of 70 GPUs, more than 1100 CPU cores, 6TB RAM and 1PB NAS.
Retrospective k-space Subsampling schemes For Deep MRI Reconstruction
In our new publication, we investigate and compare various retrospective k-space subsampling patterns and their effect on the quality of DL-based reconstructions. Our findings suggest that non-rectilinear and non-Cartesian subsampling patterns may be more suitable for DL-based reconstructions.
New A100 80GB server installed
Another compute node has been installed in the AI for Oncology Cluster kosmos. The server, nicknamed euctemon, consists of 8xA100 80G, dual CPU and 1TB of memory. Euctemon joins the slurm cluster which now consists out of 16xA100 80GB, 16xA6000 48GB and 4x RTX2080Ti, and 1 PB NAS.
The research projects of the AI for Oncology Lab
Breast MRI is the most sensitive technique for breast cancer detection available. However, the costs and availability of MRI scanners limit its use. We develop algorithms for the automatic interpretation and acceleration of breast MRI.
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.
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.
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.
The people working at the AI for Oncology Lab