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024 7 _a10.1088/978-0-7503-3603-1
_2doi
035 _a(CaBNVSL)thg00083480
035 _a(OCoLC)1350649702
040 _aCaBNVSL
_beng
_erda
_cCaBNVSL
_dCaBNVSL
050 4 _aRC254.5
_b.A685 2022eb vol. 3
060 4 _aQZ 241
_bAR791 2022eb vol. 3
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 3,
_pBrain and prostate cancer /
_cedited by Ayman El-Baz, Jasjit S. Suri.
246 3 0 _aBrain and prostate 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. 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.
520 3 _aWithin 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.
521 _aScientists, 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.
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.
_970939
650 0 _aCancer
_xTreatment
_xData processing.
_970940
650 0 _aBrain
_xCancer
_xDiagnosis
_xData processing.
_970941
650 0 _aBrain
_xCancer
_xTreatment
_xData processing.
_970942
650 0 _aProstate
_xCancer
_xDiagnosis
_xData processing.
_970943
650 0 _aProstate
_xCancer
_xTreatment
_xData processing.
_970944
650 0 _aArtificial intelligence
_xMedical applications.
_94809
650 1 2 _aNeoplasms
_xdiagnosis.
_970945
650 1 2 _aNeoplasms
_xtherapy.
_970799
650 1 2 _aBrain Neoplasms
_xdiagnosis.
_970946
650 1 2 _aBrain Neoplasms
_xtherapy.
_970947
650 1 2 _aProstatic Neoplasms
_xdiagnosis.
_970948
650 1 2 _aProstatic Neoplasms
_xtherapy.
_970949
650 1 2 _aArtificial Intelligence.
_93407
650 7 _aTechnology, engineering, agriculture.
_2bicssc
_970950
650 7 _aBiomedical engineering.
_2bisacsh
_93292
700 1 _aEl-Baz, Ayman S.,
_eeditor.
_970951
700 1 _aSuri, Jasjit S.,
_eeditor.
_970952
710 2 _aInstitute of Physics (Great Britain),
_epublisher.
_911622
776 0 8 _iPrint version:
_z9780750336017
_z9780750336048
830 0 _aIOP (Series).
_pRelease 22.
_970953
830 0 _aIPEM-IOP series in physics and engineering in medicine and biology.
_970161
830 0 _aIOP ebooks.
_p2022 collection.
_970954
856 4 0 _uhttps://iopscience.iop.org/book/edit/978-0-7503-3603-1
942 _cEBK
999 _c82930
_d82930