Advances In Deep Generative Models For Medical Artificial Intelligence
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Advances in Deep Generative Models for Medical Artificial Intelligence
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.
Generative Artificial Intelligence for Biomedical and Smart Health Informatics
Author: Aditya Khamparia
language: en
Publisher: John Wiley & Sons
Release Date: 2025-01-03
Enables readers to understand the future of medical applications with generative AI and related applications Generative Artificial Intelligence for Biomedical and Smart Health Informatics delivers a comprehensive overview of the most recent generative AI-driven medical applications based on deep learning and machine learning in which biomedical data is gathered, processed, and analyzed using data augmentation techniques. This book 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. The book explores findings obtained by explainable AI techniques, with coverage of various techniques rarely reported in literature. Throughout, feedback and user experiences from physicians and medical staff, as well as use cases, are included to provide important context. The book discusses topics including privacy and security challenges in AI-enabled health informatics, biosensor-guided AI interventions in personalized medicine, regulatory frameworks and guidelines for AI-based medical devices, education and training for building responsible AI solutions in healthcare, and challenges and opportunities in integrating generative AI with wearable devices. Topics covered include: Treatment of neurological disorders using intelligent techniques and image-guided and tomography interventions for neuromuscular disorders Bio-inspired smart healthcare service frameworks with AI, machine learning, and deep learning, integration of IoT devices, and edge computing in industrial and clinical systems Traffic management and optimization in distributed environments, patient data management, disease surveillance and prediction, and telemedicine and remote monitoring Education-driven, peer-to-peer, and service-oriented architectures and transparency and accountability in medical decision-making Generative Artificial Intelligence for Biomedical and Smart Health Informatics is an essential reference for computer science researchers, medical professionals, healthcare informatics, and medical imaging researchers interested in understanding the potential of artificial intelligence and other related technologies in healthcare.
Deep Generative Models
Author: Anirban Mukhopadhyay
language: en
Publisher: Springer Nature
Release Date: 2024-02-19
This LNCS conference volume constitutes the proceedings of the third MICCAI Workshop, DGM4MICCAI 2023, Held in Conjunction with MICCAI 2023, Vancouver, BC, Canada, October 2023. The 23 full papers included in this volume were carefully reviewed and selected from 38 submissions. The conference presents topics ranging from methodology, causal inference, latent interpretation, generative factor analysis to applications such as mammography, vessel imaging, and surgical Videos.