Titlebook: Generative Adversarial Learning: Architectures and Applications; Roozbeh Razavi-Far,Ariel Ruiz-Garcia,Juergen Schmi Book 2022 The Editor(s

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,Improved Diagnostic Performance of?Arrhythmia Classification Using Conditional GAN Augmented Heartb an Electrocardiogram (ECG) signal helps in risk stratification, better medical assistance, and patient treatment. Due to privacy concerns, access to personal ECGs is restricted, hindering the development of automated computer-aided diagnosis systems. This chapter discusses an approach for generatin

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Generative Adversarial Networks for Data Augmentation in X-Ray Medical Imaging,ituations where little data or imbalanced datasets are present. There are two main reasons why some medical datasets are limited or imbalanced: either there is little data available for some rare diseases, or the privacy policy of medical organizations does not allow it to share the data. But deep l

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,Generative Adversarial Networks: A?Survey on?Training, Variants, and?Applications,mage quality when GANs are used in image processing applications. The chapter reviews state-of-the-art GANs and focuses on the main advancements that involve adjusting the loss function, modifying the training process, and adding auxiliary neural network(s). A summary of different applications of GANs is also provided.

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Counterterrorism and Cybersecurityselected GAN-based approaches in detecting malicious intrusions in an Internet of Things (IoT) network. Experiments are evaluated in terms of false alarm and missed alarm detection rates. The obtained results indicate the effectiveness of the proposed GAN-based detection approach for the respective task.

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Enrico Bernardi,Silvia Romagnolihas shown that PGGAN generates good quality synthetic X-ray images for data augmentation to balance the dataset. The resulting balanced dataset used several classification models for testing. Various state-of-the-art classification models are adopted in transfer learning and fine-tuned to test the augmentation process.

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