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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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發(fā)表于 2025-3-23 12:23:20 | 只看該作者
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發(fā)表于 2025-3-23 15:58:43 | 只看該作者
Dr. Pecker: A Deep Learning-Based Computer-Aided Diagnosis System in Medical Imagingng on their IT infrastructure design. In comparison with traditional CAD systems that are mostly standalone applications designed to solve a particular task, we explain design choices of a cloud-based CAD platform that allows for running computational intense deep learning algorithms in a cost-effic
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發(fā)表于 2025-3-23 19:27:38 | 只看該作者
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發(fā)表于 2025-3-24 10:24:24 | 只看該作者
https://doi.org/10.1007/978-3-658-39879-8e-of-the-art super-resolution methods using 2D or 3D convolution neural networks will be introduced. From the experimental results of the network introduced in this chapter, readers can not only make a comparison about the network structure but also have a general understanding about network performance.
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發(fā)表于 2025-3-24 10:46:47 | 只看該作者
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發(fā)表于 2025-3-24 19:58:04 | 只看該作者
Erratum to: Theoretische Grundlagen,ed semi-supervised approaches have recently gained the attention of the scientific community. The shift from traditional 2D to 3D architectures is also discussed, as well as the most common loss functions to improve the performance of medical image segmentation approaches. Finally, some future trends and conclusion are described.
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發(fā)表于 2025-3-25 02:16:09 | 只看該作者
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