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

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書目名稱Deep Learning and Data Labeling for Medical Applications影響因子(影響力)




書目名稱Deep Learning and Data Labeling for Medical Applications影響因子(影響力)學(xué)科排名




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書目名稱Deep Learning and Data Labeling for Medical Applications網(wǎng)絡(luò)公開度學(xué)科排名




書目名稱Deep Learning and Data Labeling for Medical Applications被引頻次




書目名稱Deep Learning and Data Labeling for Medical Applications被引頻次學(xué)科排名




書目名稱Deep Learning and Data Labeling for Medical Applications年度引用




書目名稱Deep Learning and Data Labeling for Medical Applications年度引用學(xué)科排名




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書目名稱Deep Learning and Data Labeling for Medical Applications讀者反饋學(xué)科排名

灌溉

我正派

遺傳學(xué)

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

輕推

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.

顯微鏡

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?%.

顯微鏡

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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