"Version: 20221001"--Title page verso.

Includes bibliographical references.

1. Artificial intelligence in prostate cancer treatment with image-guided radiation therapy / Yading Yuan, Ren-Dih Sheu, Tzu-Chi Tseng, James Tam, Yeh-Chi Lo and Richard Stock -- 2. Artificial-intelligence-based diagnosis of brain tumor diseases / Samir Kumar Bandyopadhyay, Vishal Goyal and Shawni Dutta -- 3. Multisite brain tumor segmentation using a unified generative adversarial network / Jia Wei, Zecheng Liu, Wenguang Yuan and Rui Li -- 4. Role of artificial intelligence in automatic segmentation of brain metastases for radiotherapy / Prabhakar Ramachandran, Venkatakrishnan Seshadri, Ben Perrett, Akash Mehta, Davide Fontanarosa, Mark Pinkham and Matthew Foote -- 5. Applications of artificial intelligence in the fields of brain and prostate cancer / Ayturk Keles and Ali Keles -- 6. AI-based non-deep learning and deep learning techniques used to accurately predict prostate cancer / Lal Hussain and Adeel Ahmed Abbasi -- 7. Intelligent brain tumor classification using deep convolutional neural networks with transfer learning / Chung-Ming Lo and Cheng-Yeh Hsieh -- 8. Big data applications in radiation oncology : challenges and opportunities / William C. Sleeman IV, Sriram Srinivasan, Preetam Ghosh, Jatinder Palta and Rishabh Kapoor -- 9. A hybrid approach to the hyperspectral classification of in vivo brain tissue : linear unmixing with spatial coherence and machine learning / Ines A. Cruz-Guerrero, Daniel U. Campos-Delgado, Aldo R. Mejia-Rodriguez, Himar Fabelo, Samuel Ortega and Gustavo M. Callico -- 10. Application and post-hoc explainability of deep convolutional neural networks for bone cancer metastasis classification in prostate patients / Serafeim Moustakidis, Charis Ntakolia, Dimitrios E. Diamantis, Nikolaos Papandrianos and Elpiniki I. Papageorgiou -- 11. Prostate cancer detection using histopathology image analysis / Sarah M. Ayyad, Mohamed Shehata, Ahmed Shalaby, Mohamed Abou El-Ghar, Mohammed Ghazal, Moumen El-Melegy, Ali Mahmoud, Nahla B. Abdel-Hamid, Labib M. Labib, H. Arafat Ali and Ayman El-Baz -- 12. Machine learning of gliomas in 3D dynamic contrast enhanced MRI : automatic segmentation and classification / Jiwoong Jason Jeong, Yang Lei, Zhen Tian, Hui Mao, Tian Liu and Xiaofeng Yang.

Within this third volume dealing with brain and prostate cancer, the editors and authors detail the latest research related to the application of artificial intelligence (AI) to cancer diagnosis and prognosis and summarize its advantages. It is the intention of the editors and authors to explore how AI assists in these activities, specifically with regard to its unprecedented accuracy, which is even higher than that of general statistical applications in oncology. Ways will also be demonstrated as to how these methods in AI are advancing the field. There have been thousands of papers written between 1995 and 2019 related to AI for cancer diagnosis and prognosis. However, to date (to the best of our knowledge) there has not yet been published a comprehensive overview of the latest findings pertaining to these AI technologies, within a single book project. Therefore, the purpose of this three-volume work, and particularly for this third volume dealing with brain and prostate cancer, is to present a compendium of these findings related to these two pervasive cancers. Within this coverage it is our hope that scientists, researchers and clinicians can successfully incorporate these techniques into other significant cancers such as pancreatic, esophageal, leukemia, melanoma, etc. Part of IPEM-IOP Series in Physics and Engineering in Medicine and Biology.

Scientists, researchers, practitioners and clinicians dedicated to the application of AI principles in the diagnosis and prognosis of brain and prostate cancer at its earliest stages.

Also available in print.

Mode of access: World Wide Web.

System requirements: Adobe Acrobat Reader, EPUB reader, or Kindle reader.

Ayman El-Baz, PhD, is Professor, Chair of the Bioengineering Department and Distinguished Scholar, Speed School of Engineering, University of Louisville, USA. His major research focus is in the fields of bioimaging modalities and computer-assisted diagnostic systems. He has developed new techniques for analyzing 3D medical images. Dr. El-Baz has authored or co-authored more than 300 technical articles and edited or co-edited over 45 books. Among his many honors and awards are becoming an AIMBE Fellow (2018) and NAI Fellow (2020). Jasjit S. Suri, PhD is an innovator, scientist and industrialist, who has conducted considerable research in the implementation of AI in biomedicine and healthcare. He has over 50 US and European patents. Dr. Suri has published over 100 journal articles related to cardiovascular disease and another 100 dealing with AI. He has also edited or co-edited over 50 books. In 2018 he was awarded the Marquis Life Time Achievement Award and the Director General's President's Gold Medal. In addition, he is an AIMBE Fellow and IEEE Fellow.

Title from PDF title page (viewed on November 9, 2022).