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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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发表于 2025-3-21 19:10:06 | 显示全部楼层 |阅读模式
书目名称GANs for Data Augmentation in Healthcare
编辑Arun Solanki,Mohd Naved
视频video
概述Oriented towards the applications and not just the theory.Contains work from some of the pioneers of GAN.Covers practical aspects with possible supported results
图书封面Titlebook: GANs for Data Augmentation in Healthcare;  Arun Solanki,Mohd Naved Book 2023 The Editor(s) (if applicable) and The Author(s), under exclusi
描述.Computer-Assisted Diagnostics (CAD) using Convolutional Neural Network (CNN) model has become an important technology in the medical industry, improving the accuracy of diagnostics. However, the lack Magnetic Resonance Imaging (MRI) data leads to the failure of the depth study algorithm. Medical records are often different because of the cost of obtaining information and the time spent consuming the information. In general, clinical data is unreliable and therefore the training of neural network methods to distribute disease across classes does not yield the desired results. Data augmentation is often done by training data to solve problems caused by augmentation tasks such as scaling, cropping, flipping, padding, rotation, translation, affine transformation, and color augmentation techniques such as brightness, contrast, saturation, and hue...Data Augmentation and Segmentation imaging using GAN can be used to provide clear images of brain, liver, chest, abdomen, and liver on an MRI. In addition, GAN shows strong promise in the field of clinical image synthesis. In many cases, clinical evaluation is limited by a lack of data and/or the cost of actual information. GAN can overcome
出版日期Book 2023
关键词GANS; Healthcare; Machine Learning; Medical Records; Generative Adversarial Network; GAN based Image Augm
版次1
doihttps://doi.org/10.1007/978-3-031-43205-7
isbn_softcover978-3-031-43207-1
isbn_ebook978-3-031-43205-7
copyrightThe Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerl
The information of publication is updating

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发表于 2025-3-21 20:18:06 | 显示全部楼层
,A Review on Mode Collapse Reducing GANs with GAN’s Algorithm and Theory, together with generator. Generator generates the data which resembles the actual data and discriminator differentiates between actual data and generated data. Due to GAN’s complex structure, it becomes very hard to train it and it faces a lot of problems. Among these problems mode collapse is a ver
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Medical Image Synthesis Using Generative Adversarial Networks,phthalmology analysis of retinal networks gives information about the status and health condition of the eyes. The entire visual system is threatened by retinal illnesses such as retinal artery and vein occlusion, which can be prevented with early detection. Many supervised and unsupervised practice
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State of the Art Framework-Based Detection of GAN-Generated Face Images,hesis tasks. GANs have had great success in replicating real data distributions, especially images, which has led to a large amount of research on the same. More false face photos are being shared online thanks to the growth of face image transformation methods that use GANs. Automated methods to re
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Data Augmentation Approaches Using Cycle Consistent Adversarial Networks,ficient amount of data for the model to learn efficiently. For this reason several data augmentation approaches have been introduced. Generative Adversarial Networks (GANs) are unsupervised generative models that have this power. These models are used to generate new instances of data by identifying
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Geometric Transformations-Based Medical Image Augmentation,that both ML and DL algorithms are capable of identifying links between enormous amounts of data, one of the jobs for which these techniques have the most potential is visual inspection. These methods, nevertheless, call for a lot of photographs, which are not always possible to capture. Techniques
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Combining Super-Resolution GAN and DC GAN for Enhancing Medical Image Generation: A Study on ImprovPatient Version). The two main layers of the skin are the dermis (the lower or inner layer) and the epidermis (the higher or outer layer) (Donaldson, 2022). The most typical cancer in the world is skin cancer, which is becoming more frequent (Shao et al., 2017). The three types of cancers are basal
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