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 |  | 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 | doi | https://doi.org/10.1007/978-3-031-43205-7 | isbn_softcover | 978-3-031-43207-1 | isbn_ebook | 978-3-031-43205-7 | copyright | The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerl |
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