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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書目名稱Machine Learning in Medical Imaging影響因子(影響力)




書目名稱Machine Learning in Medical Imaging影響因子(影響力)學科排名




書目名稱Machine Learning in Medical Imaging網(wǎng)絡(luò)公開度




書目名稱Machine Learning in Medical Imaging網(wǎng)絡(luò)公開度學科排名




書目名稱Machine Learning in Medical Imaging被引頻次




書目名稱Machine Learning in Medical Imaging被引頻次學科排名




書目名稱Machine Learning in Medical Imaging年度引用




書目名稱Machine Learning in Medical Imaging年度引用學科排名




書目名稱Machine Learning in Medical Imaging讀者反饋




書目名稱Machine Learning in Medical Imaging讀者反饋學科排名

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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

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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