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003			ES-MaUEC
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008			231113s2023 sz | fo |||| 0|eng d
020			_a9783031432057
024	7		_a10.1007/978-3-031-43205-7 _2doi
040			_aES-MaUEC _bspa _cES-MaUEC _dES-MaUEC
050		4	_aRC78.7 .D35 _b2023 EB
245	0	0	_aGANs for Data Augmentation in Healthcare _cedited by Arun Solanki, Mohd Naved
250			_a1st ed. 2023
264		1	_aCham _bSpringer International Publishing _c2023
300			_a1 recurso en línea
336			_atexto _btxt _2rdacontent
337			_aelectrónico _bc _2rdamedia
338			_arecurso electrónico _bcr _2rdacarrier
505	0		_aChapter. 1. Role of Machine learning in Detection and Classification of Leukemia: A Comparative Analysis -- Chapter. 2. A Review on Mode Collapse Reducing GANs with GAN's Algorithm and Theory -- Chapter. 3. Medical Image Synthesis using Generative Adversarial Networks -- Chapter. 4. Chest X-ray data augmentation with Generative Adversarial Networks for pneumonia and COVID diagnosis -- Chapter. 5. State of the Art Framework based Detection of GAN Generated Face Images -- Chapter. 6. Data Augmentation in Classifying Chest Radiograph Images (CXR) using DCGAN-CNN -- Chapter. 7. Data Augmentation Approaches Using Cycle Consistent Adversarial Networks -- Chapter. 8. Geometric Transformations-based Medical Image Augmentation -- Chapter. 9. Generative Adversarial Learning for Medical Thermal Imaging Analysis -- Chapter. 10. Improving Performance of a Brain Tumor Detection on MRI Images using DCGAN-based Data Augmentation and Vision Transformer(ViT) Approach -- Chapter. 11. Combining Super-Resolution GAN and DC GAN for Enhancing Medical Image Generation: A Study on Improving CNN Model Performance -- Chapter. 12. GAN for Augmenting Cardiac MRI Segmentation -- Chapter. 13. WGAN for Data Augmentation -- Chapter. 14. Image Segmentation in Medical Images by Using Semi - Supervised Methods.
520			_aComputer-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 often different because of the cost of obtaining information and the time-consuming information. In general, clinical data are unreliable, 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 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 these problems by enabling scientists and clinicians to work on beautiful and realistic images. This can improve diagnosis, prognosis, and disease. Finally, GAN highlights the potential for location of patient information with data. This is a beneficial clinical application of GAN because it can effectively protect patient confidentiality. The proposed book covers the application of GANs on medical imaging augmentation and segmentation.
988			_aSpringer_Medicine_2023
650		7	_2embne _9138439 _aDiagnóstico _xProceso de datos
856	4	0	_uhttps://go.openathens.net/redirector/universidadeuropea.es?url=https://doi.org/10.1007/978-3-031-43205-7 _zAcceso a este recurso digital (usuarios Universidad Europea de Madrid)
942			_2lcc _cLE
998			_b07/2024 _dz _ean _zSI