Zobrazeno 1 - 6
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pro vyhledávání: '"Mehrtens, Hendrik A."'
Deep Neural Networks have shown promising classification performance when predicting certain biomarkers from Whole Slide Images in digital pathology. However, the calibration of the networks' output probabilities is often not evaluated. Communicating
Externí odkaz:
http://arxiv.org/abs/2312.09719
In the past years, deep learning has seen an increase in usage in the domain of histopathological applications. However, while these approaches have shown great potential, in high-risk environments deep learning models need to be able to judge their
Externí odkaz:
http://arxiv.org/abs/2301.01054
Calibration and uncertainty estimation are crucial topics in high-risk environments. We introduce a new diversity regularizer for classification tasks that uses out-of-distribution samples and increases the overall accuracy, calibration and out-of-di
Externí odkaz:
http://arxiv.org/abs/2201.10908
Akademický článek
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Autor:
Carole H. Sudre, Christian F. Baumgartner, Adrian Dalca, Raghav Mehta, Chen Qin, William M. Wells
This book constitutes the refereed proceedings of the 5th Workshop on Uncertainty for Safe Utilization of Machine Learning in Medical Imaging, UNSURE 2023, held in conjunction with MICCAI 2023 in Vancouver, Canada, in October 2023. For this workshop
This book constitutes the refereed proceedings of the 44th DAGM German Conference on Pattern Recognition, DAGM GCPR 2022, which was held during September 27 – 30, 2022.The 37 papers presented in this volume were carefully reviewed and selected fro