Titlebook: Uncertainty for Safe Utilization of Machine Learning in Medical Imaging, and Perinatal Imaging, Plac; 3rd International Wo Carole H. Sudre,

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Uncertainty for Safe Utilization of Machine Learning in Medical Imaging, and Perinatal Imaging, Plac978-3-030-87735-4Series ISSN 0302-9743 Series E-ISSN 1611-3349

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Improving the Reliability of Semantic Segmentation of Medical Images by Uncertainty Modeling with Bat the uncertainty estimates are fed back to the system to resample the training data more densely in areas where uncertainty is high. We show in the concrete setting of a semantic segmentation task (iPS cell colony segmentation) that the proposed system is able to increase significantly the reliability of the model.

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0302-9743 n Medical Imaging, UNSURE 2021, and the 6.th .International Workshop on Preterm, Perinatal and Paediatric Image Analysis, PIPPI 2021, held in conjunction with MICCAI 2021. The conference was planned to take place in Strasbourg, France, but was held virtually due to the COVID-19 pandemic.For UNSURE 2

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Modal Uncertainty Estimation for Medical Imaging Based Diagnosism their initial assumptions. It is thus important for machine learning models to be capable of proposing different plausible predictions, along with . uncertainty measures. In this work we propose such a novel learning-based framework, named . (MUE), to learn such one-to-many relationship with faith

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Improving the Reliability of Semantic Segmentation of Medical Images by Uncertainty Modeling with Bat the uncertainty estimates are fed back to the system to resample the training data more densely in areas where uncertainty is high. We show in the concrete setting of a semantic segmentation task (iPS cell colony segmentation) that the proposed system is able to increase significantly the reliabil