Titlebook: Deep Learning and Convolutional Neural Networks for Medical Imaging and Clinical Informatics; Le Lu,Xiaosong Wang,Lin Yang Book 2019 Sprin

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書目名稱Deep Learning and Convolutional Neural Networks for Medical Imaging and Clinical Informatics影響因子(影響力)




書目名稱Deep Learning and Convolutional Neural Networks for Medical Imaging and Clinical Informatics影響因子(影響力)學(xué)科排名




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書目名稱Deep Learning and Convolutional Neural Networks for Medical Imaging and Clinical Informatics被引頻次




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書目名稱Deep Learning and Convolutional Neural Networks for Medical Imaging and Clinical Informatics年度引用




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書目名稱Deep Learning and Convolutional Neural Networks for Medical Imaging and Clinical Informatics讀者反饋




書目名稱Deep Learning and Convolutional Neural Networks for Medical Imaging and Clinical Informatics讀者反饋學(xué)科排名

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978-3-030-13971-1Springer Nature Switzerland AG 2019

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Philipp Jordan,Paula Alexandra Silva critical to guide effective patient treatment since each subtype requires distinct therapy. Image analysis?of hematoxylin and eosin (H&E)-stained whole-slide specimens of muscle biopsies are considered as a gold standard for effective IM diagnosis. Accurate segmentation of perimysium plays an impor

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https://doi.org/10.1007/978-3-030-78221-4gans?(e.g., .) or neoplasms (e.g., .) is sometimes below satisfaction, arguably because deep networks are easily disrupted by the complex and variable background regions which occupy a large fraction of the input volume. In this chapter, we propose two coarse-to-fine mechanisms which use prediction

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Lecture Notes in Computer Science image segmentation, due to the limited amount of annotated 3D data and limited computational resources. In this chapter, by rethinking the strategy to apply 3D Convolutional Neural Networks?to segment medical images, we propose a novel 3D-based coarse-to-fine?framework to efficiently tackle these c

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