News
Latest updates from the Netherlands Cancer Institute in Artificial Intelligence
Equivariant CBCT reconstruction preprint
Medical image reconstruction with learned iterative schemes is particularly compute-heavy for inherently three-dimensional modalities such as Conebeam CT, and in our latest preprint we propose a novel architecture aimed for fast and memory efficient CBCT reconstruction
Read more
Jan. 27, 2024
We are proud to share that Kevin successfully defended his PhD thesis!
Today, on the 22nd of January, Kevin Groot Lipman successfully defended his PhD thesis at Maastricht University.
Read more
Jan. 22, 2024
New preprint on self-supervised MRI reconstruction
Struggling with SSL methods in MRI reconstruction when ground truth data is unavailable? Our new pre-print on Joint Supervised and Self-supervised Learning (JSSL) might be the solution for training DL nets in the absence of fully sampled MRI data.
Read more
Nov. 30, 2023
New preprint on calibration of image segmentation algorithms
Deep learning model outputs are generally not guaranteed to be well-calibrated. We propose a new approach, called "Kandinsky conformal prediction" for efficiently calibrating image segmentation models. Our method is built upon the framework of inductive conformal prediction, and is especially geared towards low-data scenarios such as medical image segmentation.
Read more
Nov. 20, 2023
aiEMBRACE project started
On the 14th of November, we had the kickoff meeting of our aiEMBRACE project (AI-Enhanced personalized image-based breast cancer management). In this project, we will develop and validate models to improve the risk prediction, therapy response, and early recurrence of breast cancer.
Read more
Nov. 14, 2023
Cardiac MRI Reconstruction Winners at CMRxRecon Challenge
The AI for Oncology group at The Netherlands Cancer Institute secured the 2nd position in both tasks of the CMRxRecon Challenge during MICCAI 2023, demonstrating a novel 4D method for high-speed cardiac MRI reconstruction.
Read more
Oct. 12, 2023
New preprint on Accelerated MRI Reconstruction
Our latest preprint in MRI Reconstruction presents "vSHARP: A DL-Based Approach for Accelerated Parallel MRI Reconstruction," a novel method for solving complex inverse problems in medical imaging. vSHARP combines mathematical techniques for state-of-the-art MRI reconstruction.
Read more
Sept. 18, 2023
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.
Read more
July 26, 2023
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.
Read more
May 19, 2023