Titlebook: Breast Imaging; 13th International W Anders Tingberg,Kristina L?ng,Pontus Timberg Conference proceedings 2016 Springer International Publis

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書目名稱Breast Imaging影響因子(影響力)




書目名稱Breast Imaging影響因子(影響力)學(xué)科排名




書目名稱Breast Imaging網(wǎng)絡(luò)公開度




書目名稱Breast Imaging網(wǎng)絡(luò)公開度學(xué)科排名




書目名稱Breast Imaging被引頻次




書目名稱Breast Imaging被引頻次學(xué)科排名




書目名稱Breast Imaging年度引用




書目名稱Breast Imaging年度引用學(xué)科排名




書目名稱Breast Imaging讀者反饋




書目名稱Breast Imaging讀者反饋學(xué)科排名

Allege

Automatic Microcalcification Detection in Multi-vendor Mammography Using Convolutional Neural Networcquired on mammography systems from three manufactures: Hologic, GE, and Siemens. Receiver operating characteristics analysis was carried out. Over the whole specificity range, the CNN approach yielded a higher sensitivity compared to the cascade classifier. Significantly higher mean sensitivities w

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極大痛苦

Introduction to Construct Measurement,e been performed on a GE Senoclaire, two Hologic Selenia Dimensions and two Siemens Mammomat Inspiration units. In addition to the physics tests, we have remotely collected 15 months of daily QC data on the GE unit and 2 and 3 months on the Hologic units.

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Behavior Control (in R&D Organizations),cquired on mammography systems from three manufactures: Hologic, GE, and Siemens. Receiver operating characteristics analysis was carried out. Over the whole specificity range, the CNN approach yielded a higher sensitivity compared to the cascade classifier. Significantly higher mean sensitivities w

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外觀

Accounting Control (in R&D Organizations),4?%. Statistically significant differences were observed in sensitivity for control (median?=?0.85) vs 15?% (median?=?0.65, z?=?-2.381, .?=?0.017), 15?% vs 10?% (median?=?0.55, z?=?-2.428, .?=?0.015) and control vs 10?% (z?=?-2.381, .?=?0.017). ROC AUC was significantly different for control (median

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暫時(shí)過來

Behavior Control (in R&D Organizations),cs before and after applying LUT-QNE. Performance was also tested as a preprocessing step in two microcalcification detection schemes. Results show that the proposed method statistically significantly improves microcalcification detection performance.