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001 | 9780750335959 | ||
003 | IOP | ||
005 | 20230516170320.0 | ||
006 | m eo d | ||
007 | cr cn |||m|||a | ||
008 | 221109s2022 enka fob 000 0 eng d | ||
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_a9780750335959 _qebook |
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_a10.1088/978-0-7503-3595-9 _2doi |
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035 | _a(CaBNVSL)thg00083478 | ||
035 | _a(OCoLC)1350649730 | ||
040 |
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_aRC254.5 _b.A685 2022eb vol. 1 |
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_aQZ 241 _bAR791 2022eb vol. 1 |
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_aTEC059000 _2bisacsh |
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_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] |
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300 |
_a1 online resource (various pagings) : _billustrations (some color). |
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_atext _2rdacontent |
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_aelectronic _2isbdmedia |
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_aonline resource _2rdacarrier |
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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 |
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650 | 0 |
_aCancer _xTreatment _xData processing. _970909 |
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650 | 0 |
_aLungs _xCancer _xDiagnosis _xData processing. _970910 |
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650 | 0 |
_aLungs _xCancer _xTreatment _xData processing. _970911 |
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650 | 0 |
_aKidneys _xCancer _xDiagnosis _xData processing. _970912 |
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650 | 0 |
_aKidneys _xCancer _xTreatment _xData processing. _970913 |
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650 | 0 |
_aArtificial intelligence _xMedical applications. _94809 |
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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 |
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650 | 7 |
_aBiomedical engineering. _2bisacsh _93292 |
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700 | 1 |
_aEl-Baz, Ayman S., _eeditor. _970919 |
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700 | 1 |
_aSuri, Jasjit S., _eeditor. _970920 |
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710 | 2 |
_aInstitute of Physics (Great Britain), _epublisher. _911622 |
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776 | 0 | 8 |
_iPrint version: _z9780750335935 _z9780750335966 |
830 | 0 |
_aIOP (Series). _pRelease 22. _970921 |
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830 | 0 |
_aIPEM-IOP series in physics and engineering in medicine and biology. _970161 |
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830 | 0 |
_aIOP ebooks. _p2022 collection. _970922 |
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856 | 4 | 0 | _uhttps://iopscience.iop.org/book/edit/978-0-7503-3595-9 |
942 | _cEBK | ||
999 |
_c82928 _d82928 |