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Advances in deep generative...
Generative Artificial Intelligence is rapidly advancing with many state-of-the-art performances on computer vision, speech processing, and natural language processing tasks. Generative adversarial networks and neural diffusion models can generate high-quality synthetic images of human faces, artworks, and coherent essays on different topics. Generative models are also transforming Medical Artificial Intelligence, given their potential to learn complex features from medical imaging and healthcare data. Hence, computer-aided diagnosis and healthcare are benefiting from Medical Artificial Intelligence and Generative Artificial Intelligence. This book presents the recent advances in generative models for Medical Artificial Intelligence. It covers many applications of generative models for medical image data, including volumetric medical image segmentation, data augmentation, MRI reconstruction, and modeling of spatiotemporal medical data. This book highlights the recent advancements in Generative Artificial Intelligence for medical and healthcare applications, using medical imaging and clinical and electronic health records data. Furthermore, the book comprehensively presents the concepts and applications of deep learning-based artificial intelligence methods, such as generative adversarial networks, convolutional neural networks, and vision transformers. It also presents a quantitative and qualitative analysis of data augmentation and synthesis performances of Generative Artificial Intelligence models. This book is the result of the collaborative efforts and hard work of many minds who contributed to it and illuminated the vast landscape of Medical Artificial Intelligence. The book is suitable for reading by computer science researchers, medical professionals, healthcare informatics, and medical imaging researchers interested in understanding the potential of artificial intelligence in healthcare. It serves as a compass for navigating the artificial intelligence-driven healthcare landscape
Monografía
monografia Rebiun36894179 https://catalogo.rebiun.org/rebiun/record/Rebiun36894179 m o d cr cnu|||unuuu 231219s2023 sz a o 000 0 eng d 1415748347 9783031463419 electronic bk.) 3031463412 electronic bk.) 9783031463402 3031463404 10.1007/978-3-031-46341-9 doi AU@ 000075623155 GW5XE eng rda pn GW5XE OCLCO YDX OCLCF N$T OCLCA UKAHL UKKRT UYQ bicssc TEC009000 bisacsh UYQ thema 610.285/63 23/eng/20231219 Advances in deep generative models for medical artificial intelligence Hazrat Ali, Mubashir Husain Rehmani, Zubair Shah, editors Cham Springer [2023] Cham Cham Springer 2023 1 online resource (xvi, 248 pages) illustrations (chiefly color) 1 online resource (xvi, 248 pages) Text txt rdacontent computer c rdamedia online resource cr rdacarrier Studies in computational intelligence 1860-9503 volume 1124 Deep Learning Techniques for 3D-Volumetric Segmentation of Biomedical Images -- Analysis of GAN-based Data Augmentation for GI-Tract Disease Classification -- Deep generative adversarial network-based MRI slices reconstruction and enhancement for Alzheimer's stages classification -- Evaluating the Quality and Diversity of DCGAN-based Generatively Synthesized Diabetic Retinopathy Imagery -- Deep Learning Approaches for End-to-End Modeling of Medical Spatiotemporal Data -- Skin Cancer Classification with Convolutional Deep Neural Networks and Vision Transformers using Transfer Learning -- A New CNN-Based Deep Learning Model Approach for Skin Cancer Detection and Classification -- Machine Learning Based Miscellaneous Objects Detection With Application to Cancer Images -- Advanced deep learning for heart sounds classification Generative Artificial Intelligence is rapidly advancing with many state-of-the-art performances on computer vision, speech processing, and natural language processing tasks. Generative adversarial networks and neural diffusion models can generate high-quality synthetic images of human faces, artworks, and coherent essays on different topics. Generative models are also transforming Medical Artificial Intelligence, given their potential to learn complex features from medical imaging and healthcare data. Hence, computer-aided diagnosis and healthcare are benefiting from Medical Artificial Intelligence and Generative Artificial Intelligence. This book presents the recent advances in generative models for Medical Artificial Intelligence. It covers many applications of generative models for medical image data, including volumetric medical image segmentation, data augmentation, MRI reconstruction, and modeling of spatiotemporal medical data. This book highlights the recent advancements in Generative Artificial Intelligence for medical and healthcare applications, using medical imaging and clinical and electronic health records data. Furthermore, the book comprehensively presents the concepts and applications of deep learning-based artificial intelligence methods, such as generative adversarial networks, convolutional neural networks, and vision transformers. It also presents a quantitative and qualitative analysis of data augmentation and synthesis performances of Generative Artificial Intelligence models. This book is the result of the collaborative efforts and hard work of many minds who contributed to it and illuminated the vast landscape of Medical Artificial Intelligence. The book is suitable for reading by computer science researchers, medical professionals, healthcare informatics, and medical imaging researchers interested in understanding the potential of artificial intelligence in healthcare. It serves as a compass for navigating the artificial intelligence-driven healthcare landscape Artificial intelligence- Medical applications Intelligence artificielle- Applications en médecine Artificial intelligence- Medical applications. Ali, Hazrat editor Rehmani, Mubashir Husain 1983-) editor Shah, Zubair editor Print version 3031463404 9783031463402 (OCoLC)1396789573 Studies in computational intelligence v. 1124. 1860-9503