AI for Oncology Lab @ Netherlands Cancer Institute

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.

Application of Deep Learning in Breast Cancer Imaging

Luuk Balkenende has published the first paper of his PhD in Seminars in Nuclear Medicine on "Applications of Deep Learning in Breast Cancer Imaging" where he reviews the current usages of deep learning for mammography, ultrasound and breast MRI.