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

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書目名稱Medical Ultrasound, and Preterm, Perinatal and Paediatric Image Analysis影響因子(影響力)




書目名稱Medical Ultrasound, and Preterm, Perinatal and Paediatric Image Analysis影響因子(影響力)學(xué)科排名




書目名稱Medical Ultrasound, and Preterm, Perinatal and Paediatric Image Analysis網(wǎng)絡(luò)公開度




書目名稱Medical Ultrasound, and Preterm, Perinatal and Paediatric Image Analysis網(wǎng)絡(luò)公開度學(xué)科排名




書目名稱Medical Ultrasound, and Preterm, Perinatal and Paediatric Image Analysis被引頻次




書目名稱Medical Ultrasound, and Preterm, Perinatal and Paediatric Image Analysis被引頻次學(xué)科排名




書目名稱Medical Ultrasound, and Preterm, Perinatal and Paediatric Image Analysis年度引用




書目名稱Medical Ultrasound, and Preterm, Perinatal and Paediatric Image Analysis年度引用學(xué)科排名




書目名稱Medical Ultrasound, and Preterm, Perinatal and Paediatric Image Analysis讀者反饋




書目名稱Medical Ultrasound, and Preterm, Perinatal and Paediatric Image Analysis讀者反饋學(xué)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

BLAZE

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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