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.
Table of contents
Front matter Preface Acknowledgement About the editors
Chapter 1 Deep Learning Techniques for 3D-Volumetric Segmentation of Biomedical Images Sikandar Afridi, Muhammad Irfan Khattak, Muhammad Abeer Irfan, Atif Jan and Muhammad Asif
Chapter 2 Analysis of GAN-based Data Augmentation for GI-Tract Disease Classification Muhammad Nouman Noor, Imran Ashraf and Muhammad Nazir
Chapter 3 Deep generative adversarial network-based MRI slices reconstruction and enhancement for Alzheimer’s stages classification Venkatesh Gauri Shankar and Dilip Singh Sisodia
Chapter 4 Evaluating the Quality and Diversity of DCGAN-based Generatively Synthesized Diabetic Retinopathy Imagery Cristina-Madalina Dragan, Muhammad Muneeb Saad, Mubashir Husain Rehmani, and Ruairi O'Reilly
Chapter 5 Deep Learning Approaches for End-to-End Modeling of Medical Spatiotemporal Data Jacqueline K Harris and Russell Greiner
Chapter 6 Skin Cancer Classification with Convolutional Deep Neural Networks and Vision Transformers using Transfer Learning Muniba Ashfaq and Asif Ahmad
Chapter 7 A New CNN-Based Deep Learning Model Approach for Skin Cancer Detection and Classification Halit Çetiner and Sedat Metlek
Chapter 8 Machine Learning Based Miscellaneous Objects Detection With Application to Cancer Images Zahid Mahmood, Anees Ullah, Tahir Khan, and Ali Zahir
Chapter 9 Advanced deep learning for heart sounds classification Muhammad Salman Khan, Faiq Ahmad Khan, Kaleem Nawaz Khan, Shahid Imran Rana, Mohammed Abdulla A A Al-Hashemi
Timeline
Abstract Submission - 31 December 2022 (Now closed. Decisions have been sent out) Full Chapter submission - 31 January 2023 (07 February 2023 (Now closed. We will send out decisions by first week of April 2023) Decision notification - 31 March 2023 (07 April 2023) Revised submission - 30 April 2023 (14 May 2023) Final decision. - 20 June 2023 Camera ready submission - 30 June 2023 (Submission of LaTeX source file) First online (on Springer) - September 2023 Expected publication date - 27 December 2023
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Dr. Hazrat Ali, College of Science and Engineering, Hamad Bin Khalifa University, Doha, Qatar Dr. Mubashir Husain Rehmani, Munster Technological University, Ireland Dr. Zubair Shah, College of Science and Engineering, Hamad Bin Khalifa University, Doha, Qatar
Contact
haali2 AT hbku DOT edu DOT qa hazrat DOT ali AT live DOT com
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