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 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.
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.
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.
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.
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.
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.
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