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7 papers · 1 filter
Artificial Intelligence-Based Image Reconstruction in Cardiac Magnetic Resonance
Chen Qin, Daniel Rueckert
Artificial intelligence (AI) and Machine Learning (ML) have shown great potential in improving the medical imaging workflow, from image acquisition and reconstruction to disease di…
Unsupervised Anomaly Localization with Structural Feature-Autoencoders
Felix Meissen, Johannes Paetzold, Georgios Kaissis +1
Unsupervised Anomaly Detection has become a popular method to detect pathologies in medical images as it does not require supervision or labels for training. Most commonly, the ano…
WNet: A data-driven dual-domain denoising model for sparse-view computed tomography with a trainable reconstruction layer
Theodor Cheslerean-Boghiu, Felix C. Hofmann, Manuel Schultheiß +3
Deep learning based solutions are being succesfully implemented for a wide variety of applications. Most notably, clinical use-cases have gained an increased interest and have been…
ISLES 2022: A multi-center magnetic resonance imaging stroke lesion segmentation dataset
Moritz Roman Hernandez Petzsche, Ezequiel de la Rosa, Uta Hanning +22
Magnetic resonance imaging (MRI) is a central modality for stroke imaging. It is used upon patient admission to make treatment decisions such as selecting patients for intravenous…
blob loss: instance imbalance aware loss functions for semantic segmentation
Florian Kofler, Suprosanna Shit, Ivan Ezhov +14
Deep convolutional neural networks (CNN) have proven to be remarkably effective in semantic segmentation tasks. Most popular loss functions were introduced targeting improved volum…
Federated Learning Enables Big Data for Rare Cancer Boundary Detection
Sarthak Pati, Ujjwal Baid, Brandon Edwards +276
Although machine learning (ML) has shown promise in numerous domains, there are concerns about generalizability to out-of-sample data. This is currently addressed by centrally shar…