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Titlebook: GANs for Data Augmentation in Healthcare; Arun Solanki,Mohd Naved Book 2023 The Editor(s) (if applicable) and The Author(s), under exclusi

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樓主: incoherent
11#
發(fā)表于 2025-3-23 10:31:34 | 只看該作者
GAN for Augmenting Cardiac MRI Segmentation,es greatly between facilities, vendors, and individuals with diverse cardiovascular conditions. Because most deep learning-based segmentation algorithms are taught using a small number of ground truth annotations, they may not translate well to unseen MR images due to differences in training and tes
12#
發(fā)表于 2025-3-23 14:26:11 | 只看該作者
WGAN for Data Augmentation,on. However, it is sometimes difficult and expensive to generate such realistic synthetic data that mimic the original distribution of the data set. Therefore, augmentation of data is essential to expand the size of the data set used for learning purpose while introducing more variety in what the mo
13#
發(fā)表于 2025-3-23 20:11:27 | 只看該作者
14#
發(fā)表于 2025-3-24 01:11:53 | 只看該作者
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發(fā)表于 2025-3-24 05:28:51 | 只看該作者
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發(fā)表于 2025-3-24 07:42:32 | 只看該作者
Christoph Kochhan,Jennifer Bannertstudy consists of the introduction of generative models, the idea behind the GANs and their algorithm, problems faced by GANs while training it, mode collapse problem with brief introduction of what GANs cannot produce, and, finally, the GAN methods or algorithms which reduce the mode collapse.
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發(fā)表于 2025-3-24 12:39:28 | 只看該作者
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發(fā)表于 2025-3-24 17:37:49 | 只看該作者
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發(fā)表于 2025-3-24 22:44:40 | 只看該作者
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發(fā)表于 2025-3-25 02:38:08 | 只看該作者
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