Titlebook: Machine Learning in Medical Imaging; 9th International Wo Yinghuan Shi,Heung-Il Suk,Mingxia Liu Conference proceedings 2018 Springer Nature

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书目名称Machine Learning in Medical Imaging影响因子(影响力)




书目名称Machine Learning in Medical Imaging影响因子(影响力)学科排名




书目名称Machine Learning in Medical Imaging网络公开度




书目名称Machine Learning in Medical Imaging网络公开度学科排名




书目名称Machine Learning in Medical Imaging被引频次




书目名称Machine Learning in Medical Imaging被引频次学科排名




书目名称Machine Learning in Medical Imaging年度引用




书目名称Machine Learning in Medical Imaging年度引用学科排名




书目名称Machine Learning in Medical Imaging读者反馈




书目名称Machine Learning in Medical Imaging读者反馈学科排名

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Conference proceedings 2018CCAI 2018 in Granada, Spain, in September 2018..The 45 papers presented in this volume were carefully reviewed and selected from 82 submissions. They focus on major trends and challenges in the area of machine learning in medical imaging and aim to identify new cutting-edge techniques and their use in medical imaging. .

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Dynamic Multi-scale CNN Forest Learning for Automatic Cervical Cancer Segmentation,to the best of our knowledge, these have not been used for cervical tumor segmentation. More importantly, while the majority of innovative deep-learning works using convolutional neural networks (CNNs) focus on developing more sophisticated and robust architectures (e.g., ResNet, U-Net, GANs), there

characteristic

Multi-task Fundus Image Quality Assessment via Transfer Learning and Landmarks Detection,, including image artifact, clarity, and field definition. In this paper, we propose a multi-task deep learning framework for automated assessment of fundus image quality. The network can classify whether an image is gradable, together with interpretable information about quality factors. The propos

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End-to-End Lung Nodule Detection in Computed Tomography,ptimized for radiologists. Computer vision can capture features that is subtle to human observers, so it is desirable to design a CAD system operating on the raw data. In this paper, we proposed a deep-neural-network-based detection system for lung nodule detection in computed tomography (CT). A pri