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AI for Oncology Lab @ Netherlands Cancer Institute

Our mission is to develop artificial intelligence innovations for the improvement of cancer diagnostics and therapy

Latest news

Image for Equivariant CBCT reconstruction preprint
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

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Image for New preprint on self-supervised MRI reconstruction
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.

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Image for New preprint on calibration of image segmentation algorithms
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.

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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.

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Image for Cardiac MRI Reconstruction Winners at CMRxRecon Challenge
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.

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Image for New preprint on Accelerated 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.

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The people working at the Lab