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024 7 _a10.1088/978-0-7503-3599-7
_2doi
035 _a(CaBNVSL)thg00083479
035 _a(OCoLC)1350649695
040 _aCaBNVSL
_beng
_erda
_cCaBNVSL
_dCaBNVSL
050 4 _aRC254.5
_b.A685 2022eb vol. 2
060 4 _aQZ 241
_bAR791 2022eb vol. 2
072 7 _aT
_2bicssc
072 7 _aTEC059000
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082 0 4 _a616.99/4
_223
245 0 0 _aArtificial intelligence in cancer diagnosis and prognosis.
_nVolume 2,
_pBreast and bladder cancer /
_cedited by Ayman El-Baz, Jasjit S. Suri.
246 3 0 _aBreast and bladder cancer.
264 1 _aBristol [England] (No.2 The Distillery, Glassfields, Avon Street, Bristol, BS2 0GR, UK) :
_bIOP Publishing,
_c[2022]
300 _a1 online resource (various pagings) :
_billustrations (some color).
336 _atext
_2rdacontent
337 _aelectronic
_2isbdmedia
338 _aonline resource
_2rdacarrier
490 1 _a[IOP release $release]
490 1 _aIPEM-IOP series in physics and engineering in medicine and biology
490 1 _aIOP ebooks. [2022 collection]
500 _a"Version: 20221001"--Title page verso.
504 _aIncludes bibliographical references.
505 0 _a1. 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. Jim�enez Gaona, M.J. Rodr�iguez-�Alvarez, 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.
520 3 _aWithin 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.
521 _aScientists, researchers, practitioners and clinicians dedicated to the application of AI principles in the diagnosis and prognosis of breast and bladder cancer.
530 _aAlso available in print.
538 _aMode of access: World Wide Web.
538 _aSystem requirements: Adobe Acrobat Reader, EPUB reader, or Kindle reader.
545 _aAyman 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.
588 0 _aTitle from PDF title page (viewed on November 9, 2022).
650 0 _aCancer
_xDiagnosis
_xData processing.
_970923
650 0 _aCancer
_xTreatment
_xData processing.
_970924
650 0 _aBreast
_xCancer
_xDiagnosis
_xData processing.
_970925
650 0 _aBreast
_xCancer
_xTreatment
_xData processing.
_970926
650 0 _aBladder
_xCancer
_xDiagnosis
_xData processing.
_970927
650 0 _aBladder
_xCancer
_xTreatment
_xData processing.
_970928
650 0 _aArtificial intelligence
_xMedical applications.
_94809
650 1 2 _aNeoplasms
_xdiagnosis.
_970929
650 1 2 _aNeoplasms
_xtherapy.
_970799
650 1 2 _aBreast Neoplasms
_xdiagnosis.
_970930
650 1 2 _aBreast Neoplasms
_xtherapy.
_970931
650 1 2 _aUrinary Bladder Neoplasms
_xdiagnosis.
_970932
650 1 2 _aUrinary Bladder Neoplasms
_xtherapy.
_970933
650 1 2 _aArtificial Intelligence.
_93407
650 7 _aTechnology, engineering, agriculture.
_2bicssc
_970934
650 7 _aBiomedical engineering.
_2bisacsh
_93292
700 1 _aEl-Baz, Ayman S.,
_eeditor.
_970935
700 1 _aSuri, Jasjit S.,
_eeditor.
_970936
710 2 _aInstitute of Physics (Great Britain),
_epublisher.
_911622
776 0 8 _iPrint version:
_z9780750335973
_z9780750336000
830 0 _aIOP (Series).
_pRelease 22.
_970937
830 0 _aIPEM-IOP series in physics and engineering in medicine and biology.
_970161
830 0 _aIOP ebooks.
_p2022 collection.
_970938
856 4 0 _uhttps://iopscience.iop.org/book/edit/978-0-7503-3599-7
942 _cEBK
999 _c82929
_d82929