News

Latest updates from the AI for Oncology Lab

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.

April 24, 2023

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.

Jan. 24, 2023

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.

Sept. 23, 2022

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.

Sept. 1, 2022

Our RecurrentVarNet wins the Multi-Coil MRI Reconstruction Challenge

The Recurrent Variational Network was ranked as the top method in the Multi-Coil MRI Reconstruction (MC-MRI) Challenge. The corresponding paper which assessed the generalizability of brain MRI Reconstruction models to varying coil configurations was published in the Frontiers in Neuroinformatics.

July 6, 2022

Recurrent Variational Network presented at CVPR

Our paper "Recurrent Variational Network: A Deep Learning Inverse Problem Solver applied to the task of Accelerated MRI Reconstruction" has been accepted for publication at CVPR 2022! CVPR is the top ranked Computer Science conference with leading h5-index and impact score! Our work proposes a novel DL Inverse Problem solver, the RecurrentVarNet, employed and evaluated in the essential task of Accelerated MRI Reconstruction achieving SOTA results!

June 19, 2022

Open-source Deep MRI Reconstruction software published

Our open-source Deep Image REConstruction Toolkit (DIRECT) has been accepted for publication in the Journal of Open Source Software (JOSS)! DIRECT stores multiple DL model baselines such as the Recurrent Variational Network, implemented in PyTorch for end-to-end Accelerated MRI Reconstruction tasks and allows for use with datasets such as the fastMRI and Calgary Campinas Datasets!

May 30, 2022

DeepSMILE published in Medical Image Analysis

In this work we use whole-slide image (WSI) compression and multiple instance learning to predict homologous recombination deficiency and microsatellite instability from breast cancer and colorectal cancer WSIs. Both these labels are closely related to a patient’s response to immune- and targeted therapies.

May 15, 2022

AI for Oncology present at SPIE Medical Imaging

The AI for Oncology Lab was present at SPIE Medical Imaging 2022 in San Diego, presenting a diversity of work. Shannon Doyle explored the use of self-supervised techniques for the detection of ducts in histopathological surgical specimens, Yoni Schirris presented a weak-label approach to predict the tumor-infiltrating lymphocytes score and George Yiasemis presented a deep learning-based accelerated MRI reconstruction algorithm.

Feb. 23, 2022

Last week the pre-selected three best candidates from each UvA faculty competed against each other. Yoni Schirris was the overall winner with his pitch on AI and cancer histopathology. He is working to determine which cancer patients may benefit from immunotherapy.

Sept. 13, 2021