Titlebook: Deep Learning and Convolutional Neural Networks for Medical Image Computing; Precision Medicine, Le Lu,Yefeng Zheng,Lin Yang Book 2017 Spr

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书目名称Deep Learning and Convolutional Neural Networks for Medical Image Computing影响因子(影响力)




书目名称Deep Learning and Convolutional Neural Networks for Medical Image Computing影响因子(影响力)学科排名




书目名称Deep Learning and Convolutional Neural Networks for Medical Image Computing网络公开度




书目名称Deep Learning and Convolutional Neural Networks for Medical Image Computing网络公开度学科排名




书目名称Deep Learning and Convolutional Neural Networks for Medical Image Computing被引频次




书目名称Deep Learning and Convolutional Neural Networks for Medical Image Computing被引频次学科排名




书目名称Deep Learning and Convolutional Neural Networks for Medical Image Computing年度引用




书目名称Deep Learning and Convolutional Neural Networks for Medical Image Computing年度引用学科排名




书目名称Deep Learning and Convolutional Neural Networks for Medical Image Computing读者反馈




书目名称Deep Learning and Convolutional Neural Networks for Medical Image Computing读者反馈学科排名

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Review of Deep Learning Methods in Mammography, Cardiovascular, and Microscopy Image Analysisnt role in disease detection and computer-aided decision-making. Machine learning techniques have powered many aspects in medical investigations and clinical practice. Recently, deep learning is emerging a leading machine learning tool in computer vision and begins attracting considerable attentions

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A Novel Cell Detection Method Using Deep Convolutional Neural Network and Maximum-Weight Independentocedures. In this chapter, we propose a novel algorithm for general cell detection problem: First, a set of cell detection candidates is generated using different algorithms with varying parameters. Second, each candidate is assigned a score by a trained deep convolutional neural network (DCNN). Fin

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Deep Learning for Histopathological Image Analysis: Towards Computerized Diagnosis on Cancerspathological tissue specimens. For a number of cancers, the clinical cancer grading system is highly correlated with the pathomic features of histologic primitives that appreciated from histopathological images. However, automated detection and segmentation of histologic primitives is pretty challen

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