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Institute for Artificial Intelligence in Medicine (IKIM), University Medicine Essen, Essen, Germany, Faculty of Computer Science, University of Duisburg-Essen, Essen, Germany, Department of Physics, TU Dortmund University, Dortmund, Germany, Lamarr Institute for Machine Learning and Artificial Intelligence, TU Dortmund University, Germany
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Clinicians accepted 96.5% of extracted clinical data, showcasing a breakthrough in reliable information extraction from complex patient contexts.
Registration-based data augmentation can boost medical segmentation performance by over 20% in data-scarce scenarios.
Segmenting breast lesions directly from MRI k-space makes models far more robust to undersampling and noise, without sacrificing accuracy at full sampling.
Throw out your magnitude-only MRI models: this generative approach proves that modeling the full complex-valued data unlocks superior performance in downstream diagnostic tasks.
