UID:
almahu_9949408808902882
Umfang:
1 online resource (various pagings) :
,
illustrations (some color).
ISBN:
9780750335959
,
9780750335942
Serie:
[IOP release $release]
Inhalt:
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.
Anmerkung:
"Version: 20221001"--Title page verso.
,
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.
,
Also available in print.
,
Mode of access: World Wide Web.
,
System requirements: Adobe Acrobat Reader, EPUB reader, or Kindle reader.
Weitere Ausg.:
Print version: ISBN 9780750335935
Weitere Ausg.:
ISBN 9780750335966
Sprache:
Englisch
DOI:
10.1088/978-0-7503-3595-9
URL:
https://iopscience.iop.org/book/edit/978-0-7503-3595-9
