Artificial intelligence in cancer diagnosis and prognosis. Volume 2, Breast and bladder cancer /
Breast and bladder cancer.
edited by Ayman El-Baz, Jasjit S. Suri.
- 1 online resource (various pagings) : illustrations (some color).
- [IOP release $release] IPEM-IOP series in physics and engineering in medicine and biology IOP ebooks. [2022 collection] .
- IOP (Series). Release 22. IPEM-IOP series in physics and engineering in medicine and biology. IOP ebooks. 2022 collection. .
"Version: 20221001"--Title page verso.
Includes bibliographical references.
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. 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.
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.
Scientists, researchers, practitioners and clinicians dedicated to the application of AI principles in the diagnosis and prognosis of breast and bladder cancer.
Mode of access: World Wide Web.
System requirements: Adobe Acrobat Reader, EPUB reader, or Kindle reader.
Ayman 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.
9780750335997 9780750335980
10.1088/978-0-7503-3599-7 doi
Cancer--Diagnosis--Data processing.
Cancer--Treatment--Data processing.
Breast--Cancer--Diagnosis--Data processing.
Breast--Cancer--Treatment--Data processing.
Bladder--Cancer--Diagnosis--Data processing.
Bladder--Cancer--Treatment--Data processing.
Artificial intelligence--Medical applications.
Neoplasms--diagnosis.
Neoplasms--therapy.
Breast Neoplasms--diagnosis.
Breast Neoplasms--therapy.
Urinary Bladder Neoplasms--diagnosis.
Urinary Bladder Neoplasms--therapy.
Artificial Intelligence.
Technology, engineering, agriculture.
Biomedical engineering.
RC254.5 / .A685 2022eb vol. 2
616.99/4
QZ 241 / AR791 2022eb vol. 2
"Version: 20221001"--Title page verso.
Includes bibliographical references.
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. 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.
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.
Scientists, researchers, practitioners and clinicians dedicated to the application of AI principles in the diagnosis and prognosis of breast and bladder cancer.
Mode of access: World Wide Web.
System requirements: Adobe Acrobat Reader, EPUB reader, or Kindle reader.
Ayman 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.
9780750335997 9780750335980
10.1088/978-0-7503-3599-7 doi
Cancer--Diagnosis--Data processing.
Cancer--Treatment--Data processing.
Breast--Cancer--Diagnosis--Data processing.
Breast--Cancer--Treatment--Data processing.
Bladder--Cancer--Diagnosis--Data processing.
Bladder--Cancer--Treatment--Data processing.
Artificial intelligence--Medical applications.
Neoplasms--diagnosis.
Neoplasms--therapy.
Breast Neoplasms--diagnosis.
Breast Neoplasms--therapy.
Urinary Bladder Neoplasms--diagnosis.
Urinary Bladder Neoplasms--therapy.
Artificial Intelligence.
Technology, engineering, agriculture.
Biomedical engineering.
RC254.5 / .A685 2022eb vol. 2
616.99/4
QZ 241 / AR791 2022eb vol. 2