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Artificial intelligence in cancer diagnosis and prognosis. Volume 1, Lung and kidney cancer
Title
Artificial intelligence in cancer diagnosis and prognosis. Volume 1, Lung and kidney cancer / edited by Ayman El-Baz, Jasjit S. Suri.
ISBN
9780750335959
9780750335942
9780750335935
9780750335966
Publication
Bristol [England] (No.2 The Distillery, Glassfields, Avon Street, Bristol, BS2 0GR, UK) : IOP Publishing, [2022]
Physical Description
1 online resource : illustrations (some color).
Local Notes
Access is available to the Yale community.
Notes
"Version: 20221001"--Title page verso.
Access and use
Access restricted by licensing agreement.
Biographical / Historical Note
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.
Summary
Within this first volume dealing with lung and kidney 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 first volume dealing with lung and kidney 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.
Variant and related titles
Lung and kidney cancer.
IOP ebooks.
Other formats
Also available in print.
Print version:
Format
Books / Online
Language
English
Added to Catalog
December 15, 2022
Series
[IOP release $release]
IPEM-IOP series in physics and engineering in medicine and biology
IOP ebooks. [2022 collection]
Bibliography
Includes bibliographical references.
Audience
Scientists, researchers, practitioners and clinicians dedicated to the application of AI principles in the diagnosis and prognosis of lung and kidney cancer at its earliest stages.
Contents
1. American Joint Committee on Cancer staging of lung and renal cancers using a recurrent deep neural network model / Dipanjan Moitra
2. Neural-ensemble-based detection : a modern way to diagnose lung cancer / Sharayu Govardhane, Sahil Gandhi and Pravin Shende
3. Computed tomography and magnetic resonance imaging machine learning applications for renal cell carcinoma / Elvira Guerriero, Arnaldo Stanzione, Lorenzo Ugga and Renato Cuocolo
4. Pulmonary nodule-based feature learning for automated lung tumor grading using convolutional neural networks / Supriya Suresh and Subaji Mohan
5. Detection of lung contours using closed principal curves and machine learning / Tao Peng, Yihuai Wang, Thomas Canhao Xu, Lianmin Shi, Jianwu Jiang and Shilang Zhu
6. Bytes, pixels, and bases : machine learning in imaging-omics for renal cell carcinoma / Ruchi Chauhan, C.V. Jawahar and P.K. Vinod
7. Detection, growth quantification, and malignancy prediction of pulmonary nodules using deep convolutional networks in follow-up CT scans / Xavier Rafael-Palou, Anton Aubanell, Mario Ceresa, Vicent Ribas, Gemma Piella and Miguel A González Ballester
8. Training a deep multiview model using small samples of medical data / Junzhou Huang, Xinliang Zhu and Jiawen Yao
9. Overview of deep learning for lung cancer diagnosis / Boran Sekeroglu, Daniel Chwaifo Malann and Kubra Tuncal
10. Artificial intelligence for cancer diagnosis / Sura Khalil Abd, Mustafa Musa Jaber, Sarah Yahya Ali and Mohammed Hasan Ali
11. Lung cancer diagnosis using 3D-CNN and spherical harmonics expansions / Ahmed Shaffie, Ahmed Soliman, Ali Mahmoud, Fatma Taher, Mohammed Ghazal and Ayman El-Baz.
Subjects
Subjects (Medical)
Lung Neoplasms - diagnosis.
Lung Neoplasms - therapy.
Kidney Neoplasms - diagnosis.
Kidney Neoplasms - therapy.
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