Titlebook: Intravascular Imaging and Computer Assisted Stenting, and Large-Scale Annotation of Biomedical Data ; 6th Joint Internatio M. Jorge Cardoso

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Joseph G. Jacobs,Gabriel J. Brostow,Alex Freeman,Daniel C. Alexander,Eleftheria Panagiotaki. Equations and figurers quantify the phenomena being described and provide the reader with the tools to tradeoff various performance features. The discussions 978-3-031-00406-3978-3-031-01534-2Series ISSN 1932-6076 Series E-ISSN 1932-6084

FLASK

Alison Q. O’Neil,John T. Murchison,Edwin J. R. van Beek,Keith A. Goatman. Equations and figurers quantify the phenomena being described and provide the reader with the tools to tradeoff various performance features. The discussions 978-3-031-00406-3978-3-031-01534-2Series ISSN 1932-6076 Series E-ISSN 1932-6084

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0302-9743 and Visualization for Intravascular Imaging and Computer Assisted Stenting, CVII-STENT 2017, and the Second International Workshop on Large-Scale Annotation of Biomedical Data and Expert Label Synthesis, LABELS 2017, held in conjunction with the 20th International Conference on Medical Imaging and C

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外表讀作

DCNN-Based Automatic Segmentation and Quantification of Aortic Thrombus Volume: Influence of the Trabus volume assessment, starting from its segmentation based on a Deep Convolutional Neural Network (DCNN) both pre-operatively and post-operatively. The aim is to investigate several training approaches to evaluate their influence in the thrombus volume characterization.

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Expected Exponential Loss for Gaze-Based Video and Volume Ground Truth Annotationmi-supervised setting using a novel Expected Exponential loss function. We show that our framework provides superior performances on a wide range of medical image settings compared to existing strategies and that our method can be combined with current crowd-sourcing paradigms as well.

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