News
Latest updates from the AI for Oncology Lab
Breast cancer risk prediction paper "OA-BreaCR" has been selected for oral presentation at MICCAI 2024
Recently, our paper, Ordinal Learning: Longitudinal Attention Alignment Model for Predicting Time to Future Breast Cancer Events from Mammograms has been selected at MICCAI2024 for oral presentation!
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Sept. 24, 2024
AiNed XS grant awarded to Joren Brunekreef
The Dutch Research Council has announced that they have awarded one of their National Growth Fund AiNed-XS grants to Joren Brunekreef, a postdoctoral researcher in the AI for Oncology group.
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July 6, 2024
Letter to the editor on concerns of data leakage
We have recently submitted a letter to the editor of the Journal of Imaging Informatics in Medicine, and the letter was published online today. In the letter, we raise concerns about potential data leakage in one of the journal's papers on treatment response prediction from MRI data.
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May 1, 2024
DIRECT v2.0.0
We've released v2.0.0 of our deep learning-based MRI reconstruction framework DIRECT. This release includes new deep learning models, transforms, loss functions and several performance improvements.
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April 5, 2024
AI, Explanation, and Black Boxes
Our new paper on Artificial intelligence and explanation: How, why, and when to explain black boxes has been published in EJR.
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March 7, 2024
H100 server joins our AI cluster
We have added Herakles, a server with 8xH100 SXM5 GPUs, to our Kosmos cluster. This will allow us to scale our AI models significantly.
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March 6, 2024
Two papers accepted at CVPR 2024
Our papers "Task-Driven Wavelets using Constrained Empirical Risk Minimization" and "Kandinsky Conformal Prediction: Efficient Calibration of Image Segmentation Algorithms" have been accepted for publication at CVPR 2024.
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Feb. 27, 2024
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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Jan. 27, 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.
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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.
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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.
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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.
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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.
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Sept. 18, 2023