Titlebook: Deep Learning in Healthcare; Paradigms and Applic Yen-Wei Chen,Lakhmi C. Jain Book 2020 Springer Nature Switzerland AG 2020 Deep Learning.M

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Deep Learning in Healthcare978-3-030-32606-7Series ISSN 1868-4394 Series E-ISSN 1868-4408

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Destillier- und Rektifiziertechnikmon deep learning architectures for image detection are briefly explained, including scanning-based methods and end-to-end detection systems. Some considerations about the training scheme and loss functions are also included. Then, an overview of relevant publications in anatomical and pathological

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Erratum to: Theoretische Grundlagen,llenges of medical image segmentation, for which actual approaches to overcome those limitations are discussed. Secondly, supervised and semi-supervised architectures are described, where encoder-decoder type networks are the most widely employed ones. Nonetheless, generative adversarial network-bas

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Barbara Neuhofer,Lukas Grundnern. In traditional image classification, low-level or mid-level features are extracted to represent the image and a trainable classifier is then used for label assignments. In recent years, the high-level feature representation of deep convolutional neural networks has proven to be superior to hand-c

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https://doi.org/10.1007/978-3-658-39879-8 methods about convolutional layer, deconvolution layer, loss function and evaluation functions for beginners to easily understand. Then, typical state-of-the-art super-resolution methods using 2D or 3D convolution neural networks will be introduced. From the experimental results of the network intr

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https://doi.org/10.1007/978-3-658-28110-6gh CNNs have achieved state-of-the-art performances, most researches on semantic segmentation using the deep learning methods are in the field of computer vision, so the research on medical images is much less mature than that of natural images, especially, in the field of 3D image segmentation. Our