Titlebook: Medical Ultrasound, and Preterm, Perinatal and Paediatric Image Analysis; First International Yipeng Hu,Roxane Licandro,Jordina Torrents B

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书目名称Medical Ultrasound, and Preterm, Perinatal and Paediatric Image Analysis影响因子(影响力)




书目名称Medical Ultrasound, and Preterm, Perinatal and Paediatric Image Analysis影响因子(影响力)学科排名




书目名称Medical Ultrasound, and Preterm, Perinatal and Paediatric Image Analysis网络公开度




书目名称Medical Ultrasound, and Preterm, Perinatal and Paediatric Image Analysis网络公开度学科排名




书目名称Medical Ultrasound, and Preterm, Perinatal and Paediatric Image Analysis被引频次




书目名称Medical Ultrasound, and Preterm, Perinatal and Paediatric Image Analysis被引频次学科排名




书目名称Medical Ultrasound, and Preterm, Perinatal and Paediatric Image Analysis年度引用




书目名称Medical Ultrasound, and Preterm, Perinatal and Paediatric Image Analysis年度引用学科排名




书目名称Medical Ultrasound, and Preterm, Perinatal and Paediatric Image Analysis读者反馈




书目名称Medical Ultrasound, and Preterm, Perinatal and Paediatric Image Analysis读者反馈学科排名

Constant

Conference proceedings 2020rnational Workshop on Perinatal, Preterm and Paediatric Image Analysis, PIPPI 2020, held in conjunction with MICCAI 2020, the 23rd International Conference on Medical Image Computing and Computer-Assisted Intervention. The conference was planned to take place in Lima, Peru, but changed to an online

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0302-9743 e 5th International Workshop on Perinatal, Preterm and Paediatric Image Analysis, PIPPI 2020, held in conjunction with MICCAI 2020, the 23rd International Conference on Medical Image Computing and Computer-Assisted Intervention. The conference was planned to take place in Lima, Peru, but changed to

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Calibrated Bayesian Neural Networks to Estimate Gestational Age and Its Uncertainty on Fetal Brain U RMSE and MAE of 9.6 and 12.5 days respectively over the GA range. We explore the robustness of the BNN architecture to invalid input images by testing with (i) a different dataset derived from routine anomaly scanning and (ii) scans of a different fetal anatomy.

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Deep Learning for Automatic Spleen Length Measurement in Sickle Cell Disease Patients made by human experts. Our best model (segmentation-based) achieved a percentage length error of 7.42%, which is approaching the level of inter-observer variability (5.47%–6.34%). To the best of our knowledge, this is the first attempt to measure spleen size in a fully automated way from ultrasound images.

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