吸引力
发表于 2025-3-28 15:59:27
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SHOCK
发表于 2025-3-28 22:21:55
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伴随而来
发表于 2025-3-29 00:01:06
Deep Learning in Healthcare978-3-030-32606-7Series ISSN 1868-4394 Series E-ISSN 1868-4408
Fecal-Impaction
发表于 2025-3-29 03:46:47
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Merited
发表于 2025-3-29 09:52:57
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Fsh238
发表于 2025-3-29 11:29:42
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
粗鲁的人
发表于 2025-3-29 17:52:02
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
macrophage
发表于 2025-3-29 20:27:54
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
冰雹
发表于 2025-3-30 00:51:13
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
GLADE
发表于 2025-3-30 06:37:39
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