Advances In Deep Generative Modeling For Clinical Data
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Advances In Deep Generative Models For Medical Artificial Intelligence
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Author : Hazrat Ali
language : en
Publisher: Springer Nature
Release Date : 2023-12-16
Advances In Deep Generative Models For Medical Artificial Intelligence written by Hazrat Ali and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2023-12-16 with Computers categories.
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.
Advances In Deep Generative Modeling For Clinical Data
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Author : Rahul Gopalkrishnan
language : en
Publisher:
Release Date : 2020
Advances In Deep Generative Modeling For Clinical Data written by Rahul Gopalkrishnan and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2020 with categories.
The intelligent use of electronic health record data opens up new opportunities to improve clinical care. Such data have the potential to uncover new sub-types of a disease, approximate the effect of a drug on a patient, and create tools to find patients with similar phenotypic profiles. Motivated by such questions, this thesis develops new algorithms for unsupervised and semi-supervised learning of latent variable, deep generative models – Bayesian networks parameterized by neural networks. To model static, high-dimensional data, we derive a new algorithm for inference in deep generative models. The algorithm, a hybrid between stochastic variational inference and amortized variational inference, improves the generalization of deep generative models on data with long-tailed distributions. We develop gradient-based approaches to interpret the parameters of deep generative models, and fine-tune such models using supervision to tackle problems that arise in few-shot learning. To model longitudinal patient biomarkers as they vary due to treatment we propose Deep Markov Models (DMMs). We design structured inference networks for variational learning in DMMs; the inference network parameterizes a variational approximation which mimics the factorization of the true posterior distribution. We leverage insights in pharmacology to design neural architectures which improve the generalization of DMMs on clinical problems in the low-data regime. We show how to capture structure in longitudinal data using deep generative models in order to reduce the sample complexity of nonlinear classifiers thus giving us a powerful tool to build risk stratification models from complex data.
Introduction To Deep Learning For Healthcare
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Author : Cao Xiao
language : en
Publisher: Springer Nature
Release Date : 2021-11-11
Introduction To Deep Learning For Healthcare written by Cao Xiao and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2021-11-11 with Medical categories.
This textbook presents deep learning models and their healthcare applications. It focuses on rich health data and deep learning models that can effectively model health data. Healthcare data: Among all healthcare technologies, electronic health records (EHRs) had vast adoption and a significant impact on healthcare delivery in recent years. One crucial benefit of EHRs is to capture all the patient encounters with rich multi-modality data. Healthcare data include both structured and unstructured information. Structured data include various medical codes for diagnoses and procedures, lab results, and medication information. Unstructured data contain 1) clinical notes as text, 2) medical imaging data such as X-rays, echocardiogram, and magnetic resonance imaging (MRI), and 3) time-series data such as the electrocardiogram (ECG) and electroencephalogram (EEG). Beyond the data collected during clinical visits, patient self-generated/reported data start to grow thanks to wearable sensors’ increasing use. The authors present deep learning case studies on all data described. Deep learning models: Neural network models are a class of machine learning methods with a long history. Deep learning models are neural networks of many layers, which can extract multiple levels of features from raw data. Deep learning applied to healthcare is a natural and promising direction with many initial successes. The authors cover deep neural networks, convolutional neural networks, recurrent neural networks, embedding methods, autoencoders, attention models, graph neural networks, memory networks, and generative models. It’s presented with concrete healthcare case studies such as clinical predictive modeling, readmission prediction, phenotyping, x-ray classification, ECG diagnosis, sleep monitoring, automatic diagnosis coding from clinical notes, automatic deidentification, medication recommendation, drug discovery (drug property prediction and molecule generation), and clinical trial matching. This textbook targets graduate-level students focused on deep learning methods and their healthcare applications. It can be used for the concepts of deep learning and its applications as well. Researchers working in this field will also find this book to be extremely useful and valuable for their research.
Adversarial Deep Generative Techniques For Early Diagnosis Of Neurological Conditions And Mental Health Practises
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Author : Abhishek Kumar
language : en
Publisher: Springer Nature
Release Date : 2025-08-16
Adversarial Deep Generative Techniques For Early Diagnosis Of Neurological Conditions And Mental Health Practises written by Abhishek Kumar and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2025-08-16 with Computers categories.
This book explores a pioneering exploration of how deep generative models, including generative adversarial networks (GANs) and variational autoencoders (VAEs), renovating early neurological disorder detection. This book is a bridge between computational neuroscience and clinical neurology gaps, providing novel AI-driven methodologies for diagnosing conditions such as Alzheimer’s, Parkinson’s, epilepsy, and neurodevelopmental disorders. With a strong focus on neuroimaging, genomic data analysis, and biomedical informatics, the book equips researchers and practitioners with the tools to improve diagnostic accuracy and decision-making. It includes practical case studies, visual illustrations, and structured methodologies for training and validating deep learning models. Designed for neurologists, radiologists, data scientists, and AI researchers, this book is an essential resource for advancing precision medicine and next-generation healthcare innovation.
Applications Of Generative Ai
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Author : Zhihan Lyu
language : en
Publisher: Springer Nature
Release Date : 2024-03-05
Applications Of Generative Ai written by Zhihan Lyu and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2024-03-05 with Computers categories.
This book provides a comprehensive introduction to Generative AI in terms of basic concepts, core technologies, technical architecture, and application scenarios. Readers gain a deeper understanding of the emerging discipline of Generative AI. This book covers the latest cutting-edge application technologies of Generative AI in various fields. It provides relevant practitioners with ideas to solve problems and deepen their understanding of Generative AI. At the same time, it guides and helps Generative AI and related industries to deepen their understanding of the industry and enhance professional knowledge and skills. Starting from reality, this book lists many cases and analyzes theories in a popular image. The book is useful for AI researchers and specifically for those working with the applications at hand (primarily medical imaging and construction/twinning industry). It covers a variety of cutting-edge technologies in Generative AI, which provides researchers with new research ideas.
Deep Generative Models For Integrative Analysis Of Alzheimer S Biomarkers
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Author : Kumar, Abhishek
language : en
Publisher: IGI Global
Release Date : 2024-11-01
Deep Generative Models For Integrative Analysis Of Alzheimer S Biomarkers written by Kumar, Abhishek and has been published by IGI Global this book supported file pdf, txt, epub, kindle and other format this book has been release on 2024-11-01 with Medical categories.
The integration of generative AI and deep learning techniques for Alzheimer's disease detection significantly impacts the research community by advancing diagnostic accuracy and providing a comprehensive understanding of the disease. By combining multiple data modalities, including imaging, genetics, and clinical data, researchers can improve diagnostic precision and develop personalized treatment strategies. Generative AI facilitates efficient data utilization through dataset augmentation, fostering innovation and collaboration across interdisciplinary fields. These methodologies forward the exploration of new diagnostic tools while expediting their application in clinical practice, benefiting patients through early detection and intervention. The incorporation of generative AI may enhance research capabilities, promote collaboration, and improve Alzheimer's disease management and patient outcomes. Deep Generative Models for Integrative Analysis of Alzheimer's Biomarkers explores the integration of deep generative models in disease diagnosis, biomarking, and prediction. It examines the use of tools like data analysis, natural language processing, and machine learning for effective Alzheimer’s research. This book covers topics such as data analysis, biomedicine, and machine learning, and is a useful resource for computer engineers, biologists, scientists, medical professionals, healthcare workers, academicians, and researchers.
Generative Machine Learning Models In Medical Image Computing
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Author : Le Zhang
language : en
Publisher: Springer Nature
Release Date : 2025-03-12
Generative Machine Learning Models In Medical Image Computing written by Le Zhang and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2025-03-12 with Mathematics categories.
Generative Machine Learning Models in Medical Image Computing" provides a comprehensive exploration of generative modeling techniques tailored to the unique demands of medical imaging. This book presents an in-depth overview of cutting-edge generative models such as GANs, VAEs, and diffusion models, examining how they enable groundbreaking applications in medical image synthesis, reconstruction, and enhancement. Covering diverse imaging modalities like MRI, CT, and ultrasound, it illustrates how these models facilitate improvements in image quality, support data augmentation for scarce datasets, and create new avenues for predictive diagnostics. Beyond technical details, the book addresses critical challenges in deploying generative models for healthcare, including ethical concerns, interpretability, and clinical validation. With a strong focus on real-world applications, it includes case studies and implementation guidelines, guiding readers in translating theory into practice. By addressing model robustness, reproducibility, and clinical utility, this book is an essential resource for researchers, clinicians, and data scientists seeking to leverage generative models to enhance biomedical imaging and deliver impactful healthcare solutions. Combining technical rigor with practical insights, it offers a roadmap for integrating advanced generative approaches in the field of medical image computing.
Deep Generative Models
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Author : Anirban Mukhopadhyay
language : en
Publisher: Springer Nature
Release Date : 2024-10-08
Deep Generative Models written by Anirban Mukhopadhyay and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2024-10-08 with Computers categories.
This book constitutes the proceedings of the 4th workshop on Deep Generative Models for Medical Image Computing and Computer Assisted Intervention, DGM4MICCAI 2024, held in conjunction with the 27th International conference on Medical Image Computing and Computer Assisted Intervention, MICCAI 2024, in Marrakesh, Morocco in October 2024. The 21 papers presented here were carefully reviewed and selected from 40 submissions. These papers deal with a broad range of topics, ranging from methodology (such as Causal inference, Latent interpretation, Generative factor analysis) to Applications (such as Mammography, Vessel imaging, Surgical videos and more).
Generative Intelligence In Healthcare
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Author : Sakshi Gupta
language : en
Publisher: CRC Press
Release Date : 2025-04-10
Generative Intelligence In Healthcare written by Sakshi Gupta and has been published by CRC Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2025-04-10 with Technology & Engineering categories.
This book explores the intersection of artificial intelligence and healthcare with a focus on generative intelligence. The book will introduce the concept of generative intelligence and its transformative potential in patient care, showcasing applications that go beyond conventional AI approaches. Generative Intelligence in Healthcare: Transforming Patient Care with AI Creativity delves into the use of generative models for personalized medicine, data analytics, and predictive modelling, providing real-world examples of how AI creativity can revolutionize treatment strategies and diagnostic processes. It focuses on the origin and basics of generative AI, generative AI models, and possible areas in healthcare where generative AI can work. It discusses how generative AI model will help healthcare providers automatically generate prescriptions, discharge summaries, and patient conditions. The unique strength of this book lies in its comprehensive examination of ethical considerations and regulatory frameworks, ensuring a responsible and transparent integration of generative intelligence in healthcare. By addressing current challenges and envisioning future directions, this book serves as a valuable resource for healthcare professionals, researchers, and policymakers seeking to harness the full potential of AI creativity to enhance patient outcomes. The book is written for graduate students, researchers, and professionals in biomedical engineering, electrical engineering, signal process engineering, biomedical imaging, and computer science.
Advanced Data Mining And Applications
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Author : Quan Z. Sheng
language : en
Publisher: Springer Nature
Release Date : 2024-12-12
Advanced Data Mining And Applications written by Quan Z. Sheng and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2024-12-12 with Computers categories.
This six-volume set, LNAI 15387-15392, constitutes the refereed proceedings of the 20th International Conference on Advanced Data Mining and Applications, ADMA 2024, held in Sydney, New South Wales, Australia, during December 3–5, 2024. The 159 full papers presented here were carefully reviewed and selected from 422 submissions. These papers have been organized under the following topical sections across the different volumes: - Part I : Applications; Data mining. Part II : Data mining foundations and algorithms; Federated learning; Knowledge graph. Part III : Graph mining; Spatial data mining. Part IV : Health informatics. Part V : Multi-modal; Natural language processing. Part VI : Recommendation systems; Security and privacy issues.