Titlebook: Deep Learning and Data Labeling for Medical Applications; First International Gustavo Carneiro,Diana Mateus,Julien Cornebise Conference pr

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书目名称Deep Learning and Data Labeling for Medical Applications影响因子(影响力)




书目名称Deep Learning and Data Labeling for Medical Applications影响因子(影响力)学科排名




书目名称Deep Learning and Data Labeling for Medical Applications网络公开度




书目名称Deep Learning and Data Labeling for Medical Applications网络公开度学科排名




书目名称Deep Learning and Data Labeling for Medical Applications被引频次




书目名称Deep Learning and Data Labeling for Medical Applications被引频次学科排名




书目名称Deep Learning and Data Labeling for Medical Applications年度引用




书目名称Deep Learning and Data Labeling for Medical Applications年度引用学科排名




书目名称Deep Learning and Data Labeling for Medical Applications读者反馈




书目名称Deep Learning and Data Labeling for Medical Applications读者反馈学科排名

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遗传学

0302-9743 n Medical Image Computing and Computer-Assisted Intervention, MICCAI 2016, in Athens, Greece, in October 2016: the First Workshop on Large-Scale Annotation of Biomedical Data and Expert Label Synthesis, LABELS 2016, and the Second International Workshop on Deep Learning in Medical Image Analysis, DL

轻推

Roundtable: A Discussion About Design/Repairaluations. Object search evidence obtained from three orientations and different learning architectures is consolidated through fusion schemes to lead to the target organ location. Experiments conducted using 499 patient CT body scans show promise and robustness of the proposed approach.

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https://doi.org/10.1007/978-3-031-01598-4ss validation was used giving an average accuracy of 94.5 %, a major improvement from previous methods which had an accuracy of 84 % on the same dataset. The method was also validated on a dataset of the carotid artery to show that the method can generalize to blood vessels on other regions of the body. The accuracy on this dataset was 96 %.

显微镜

Designed Technologies for Healthy Agingss longitudinal data, a novel contribution in the domain of MS lesion analysis. The method was tested on the ISBI 2015 dataset and obtained state-of-the-art Dice results with the performance level of a trained human rater.

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INCUR

Vincent Jeanne,Maarten Bodlaendereriority of the FCN over all other methods tested. Using our fully automatic algorithm we achieved true positive rate of 0.86 and 0.6 false positive per case which are very promising and clinically relevant results.

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