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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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发表于 2025-3-21 16:18:31 | 显示全部楼层 |阅读模式
书目名称Deep Learning and Convolutional Neural Networks for Medical Imaging and Clinical Informatics
编辑Le Lu,Xiaosong Wang,Lin Yang
视频video
概述Reviews the state of the art in deep learning approaches to robust disease detection, organ segmentation in medical image computing, and the construction and mining of large-scale radiology databases.
丛书名称Advances in Computer Vision and Pattern Recognition
图书封面Titlebook: Deep Learning and Convolutional Neural Networks for Medical Imaging and Clinical Informatics; Le Lu,Xiaosong Wang,Lin Yang Book 2019 Sprin
描述This book reviews the state of the art in deep learning approaches to high-performance robust disease detection, robust and accurate organ segmentation in medical image computing (radiological and pathological imaging modalities), and the construction and mining of large-scale radiology databases. It particularly focuses on the application of convolutional neural networks, and on recurrent neural networks like LSTM, using numerous practical examples to complement the theory. .The book’s chief features are as follows: It highlights how deep neural networks can be used to address new questions and protocols, and to tackle current challenges in medical image computing; presents a comprehensive review of the latest research and literature; and describes a range of different methods that employ deep learning for object or landmark detection tasks in 2D and 3D medical imaging. In addition, the book examines a broad selection of techniques for semantic segmentation using deep learning principles in medical imaging; introduces a novel approach to text and image deep embedding for a large-scale chest x-ray image database; and discusses how deep learning relational graphs can be used to orga
出版日期Book 2019
关键词Deep Learning; Convolutional Neural Networks; Medical Image Analytics; Computer-Aided Diagnosis; Hospita
版次1
doihttps://doi.org/10.1007/978-3-030-13969-8
isbn_softcover978-3-030-13971-1
isbn_ebook978-3-030-13969-8Series ISSN 2191-6586 Series E-ISSN 2191-6594
issn_series 2191-6586
copyrightSpringer Nature Switzerland AG 2019
The information of publication is updating

书目名称Deep Learning and Convolutional Neural Networks for Medical Imaging and Clinical Informatics影响因子(影响力)




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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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