000 07589nam a2200805 i 4500
001 9780750335959
003 IOP
005 20230516170320.0
006 m eo d
007 cr cn |||m|||a
008 221109s2022 enka fob 000 0 eng d
020 _a9780750335959
_qebook
020 _a9780750335942
_qmobi
020 _z9780750335935
_qprint
020 _z9780750335966
_qmyPrint
024 7 _a10.1088/978-0-7503-3595-9
_2doi
035 _a(CaBNVSL)thg00083478
035 _a(OCoLC)1350649730
040 _aCaBNVSL
_beng
_erda
_cCaBNVSL
_dCaBNVSL
050 4 _aRC254.5
_b.A685 2022eb vol. 1
060 4 _aQZ 241
_bAR791 2022eb vol. 1
072 7 _aT
_2bicssc
072 7 _aTEC059000
_2bisacsh
082 0 4 _a616.99/4
_223
245 0 0 _aArtificial intelligence in cancer diagnosis and prognosis.
_nVolume 1,
_pLung and kidney cancer /
_cedited by Ayman El-Baz, Jasjit S. Suri.
246 3 0 _aLung and kidney 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. 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 Gonz�alez 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.
520 3 _aWithin 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.
521 _aScientists, 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.
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.
_970908
650 0 _aCancer
_xTreatment
_xData processing.
_970909
650 0 _aLungs
_xCancer
_xDiagnosis
_xData processing.
_970910
650 0 _aLungs
_xCancer
_xTreatment
_xData processing.
_970911
650 0 _aKidneys
_xCancer
_xDiagnosis
_xData processing.
_970912
650 0 _aKidneys
_xCancer
_xTreatment
_xData processing.
_970913
650 0 _aArtificial intelligence
_xMedical applications.
_94809
650 1 2 _aNeoplasms
_xdiagnosis.
_970914
650 1 2 _aNeoplasms
_xtherapy.
_970799
650 1 2 _aLung Neoplasms
_xdiagnosis.
_970359
650 1 2 _aLung Neoplasms
_xtherapy.
_970915
650 1 2 _aKidney Neoplasms
_xdiagnosis.
_970916
650 1 2 _aKidney Neoplasms
_xtherapy.
_970917
650 1 2 _aArtificial Intelligence.
_93407
650 7 _aTechnology, engineering, agriculture.
_2bicssc
_970918
650 7 _aBiomedical engineering.
_2bisacsh
_93292
700 1 _aEl-Baz, Ayman S.,
_eeditor.
_970919
700 1 _aSuri, Jasjit S.,
_eeditor.
_970920
710 2 _aInstitute of Physics (Great Britain),
_epublisher.
_911622
776 0 8 _iPrint version:
_z9780750335935
_z9780750335966
830 0 _aIOP (Series).
_pRelease 22.
_970921
830 0 _aIPEM-IOP series in physics and engineering in medicine and biology.
_970161
830 0 _aIOP ebooks.
_p2022 collection.
_970922
856 4 0 _uhttps://iopscience.iop.org/book/edit/978-0-7503-3595-9
942 _cEBK
999 _c82928
_d82928