Biomedical Image Registration, Domain Generalisation and Out-of-Distribution Analysis
MICCAI 2021 Challenges: MIDOG 2021, MOOD 2021, and Learn2Reg 2021, Held in Conjunction with MICCAI 2021, Strasbourg, France, September 27-October 1, 2021, Proceedings
Herausgegeben: Aubreville, Marc; Zimmerer, David; Heinrich, Mattias
PAYBACK Punkte
0 °P sammeln!
This book constitutes three challenges that were held in conjunction with the 24th International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2021, which was planned to take place in Strasbourg, France but changed to an online event due to the COVID-19 pandemic.The peer-reviewed 18 long and 9 short papers included in this volume stem from the following three biomedical image analysis challenges:Mitosis Domain Generalization Challenge (MIDOG 2021), Medical Out-of-Distribution Analysis Challenge (MOOD 2021), and Learn2Reg (L2R 2021).The challenges share the ne...
This book constitutes three challenges that were held in conjunction with the 24th International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2021, which was planned to take place in Strasbourg, France but changed to an online event due to the COVID-19 pandemic.
The peer-reviewed 18 long and 9 short papers included in this volume stem from the following three biomedical image analysis challenges:
Mitosis Domain Generalization Challenge (MIDOG 2021), Medical Out-of-Distribution Analysis Challenge (MOOD 2021), and Learn2Reg (L2R 2021).
The challenges share the need for developing and fairly evaluating algorithms that increase accuracy, reproducibility and efficiency of automated image analysis in clinically relevant applications.
The peer-reviewed 18 long and 9 short papers included in this volume stem from the following three biomedical image analysis challenges:
Mitosis Domain Generalization Challenge (MIDOG 2021), Medical Out-of-Distribution Analysis Challenge (MOOD 2021), and Learn2Reg (L2R 2021).
The challenges share the need for developing and fairly evaluating algorithms that increase accuracy, reproducibility and efficiency of automated image analysis in clinically relevant applications.
Andere Kunden interessierten sich für
