UID:
almahu_9949408808802882
Umfang:
1 online resource (various pagings) :
,
illustrations (some color).
ISBN:
9780750335997
,
9780750335980
Serie:
[IOP release $release]
Inhalt:
Within this second volume dealing with breast and bladder 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, with a single book project. Therefore, the purpose of this three-volume work, and particularly for this second volume dealing with breast and bladder cancer, is to present a compendium of these findings related to these two pervasive cancers. Many of the chapter authors are world class researchers in these technologies. 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. Development of artificial neural networks for breast histopathological image analysis / Chen Li, Yuchao Zheng, Haiqing Zhang, Xiaomin Zhou, Yin Dai and Xiaoyan Li -- 2. Machine learning in bladder cancer diagnosis / Elliot S. Kim, Valentina L. Kouznetsova and Igor F. Tsigelny -- 3. Deep learning in photoacoustic breast cancer imaging / Changchun Yang and Fei Gao -- 4. Histopathological breast cancer image classification with feature prioritization using a heuristic algorithm / Abdullah-Al Nahid, Johir Raihan, Niloy Sikder and Saifur Rahman Sabuj -- 5. The use of machine learning and biofluid metabolomics in breast cancer diagnosis / Mashiro Sugimoto -- 6. AUTO-BREAST : a fully automated pipeline for breast cancer diagnosis using AI technology / Nagia M. Ghanem, Omneya Attallah, Fatma Anwar and Mohamed A. Ismail -- 7. Diagnosis of breast cancer from histopathological images using artificial intelligence / R. Rashmi, Keerthana Prasad and Chethana Babu K. Udupa -- 8. The role of artificial intelligence in the field of bladder cancer / Agus Rizal A.H. Hamid, Prasandhya A. Yusuf and Anindya Pradipta -- 9. Exploring data science paradigms in breast cancer classification : linking data, learning, and artificial intelligence in medical diagnosis / Shomona Gracia Jacob and Bensujin Bennet -- 10. Automatic detection and classification of invasive ductal carcinoma in histopathology images using convolutional neural networks / R. Karthiga, K. Narasimhan and N. Raju -- 11. Machine learning analysis of breast cancer single-cell omics data / Shenghua Tian and Tao Huang -- 12. Radiomics, deep learning, and breast cancer detection / Y. Jiménez Gaona, M.J. Rodríguez-Álvarez, D. Castillo Malla and V. Lakshminarayanan -- 13. Artificial-intelligence-based techniques for the diagnosis of bladder and breast cancer / Shadab Momin, Yang Lei, Tian Liu and Xiaofeng Yang.
,
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 9780750335973
Weitere Ausg.:
ISBN 9780750336000
Sprache:
Englisch
DOI:
10.1088/978-0-7503-3599-7
URL:
https://iopscience.iop.org/book/edit/978-0-7503-3599-7
