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Latest updates from the AI for Oncology Lab

Image for Letter to the editor on concerns of data leakage
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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Image for DIRECT v2.0.0
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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Image for H100 server joins our AI cluster
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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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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Image for New publication about breast cancer primary treatment response assessment and prediction
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.

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Image for New preprint about Constrained Empirical Risk Minimization
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.

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