Deep learning technology for improving cancer care in society: New directions in cancer imaging driven by artificial intelligence

Technology in Society - Tập 60 - Trang 101198 - 2020
Mario Coccia1
1National Research Council of Italy & Yale University, Yale University School of Medicine, Global Oncology, Yale Comprehensive Cancer Center, 310 Cedar Street, Lauder Hall, Suite 118, New Haven, CT, 06510, USA

Tài liệu tham khảo

Goodfellow, 2018 Iafrate, 2018 O'Leary, 2013, Artificial intelligence and big data, IEEE Intell. Syst., 28, 96, 10.1109/MIS.2013.39 Kantarjian, 2015, Artificial intelligence, big data, and cancer, JAMA Oncol., 1, 10.1001/jamaoncol.2015.1203 Bi, 2019, Artificial intelligence in cancer imaging: clinical challenges and applications, CA cancer, J. Clin., 0, 1 Gulshan, 2016, Development and validation of a deep learning algorithm for detection of diabetic retinopathy in retinal fundus photographs, J. Am. Med. Assoc., 316, 2402, 10.1001/jama.2016.17216 Johnson, 2019, Survey on deep learning with class imbalance, J. Big Data, 6, 1, 10.1186/s40537-019-0192-5 Sonntag, 2019, Artificial intelligence in medicine, HNO, 67, 343, 10.1007/s00106-019-0665-z Madabhushi, 2016, Image analysis and machine learning in digital pathology: challenges and opportunities, Med. Image Anal., 33, 170, 10.1016/j.media.2016.06.037 Litjens, 2016, Deep learning as a tool for increased accuracy and efficiency of histopathological diagnosis, Sci. Rep., 6, 26286, 10.1038/srep26286 Zhu, 2019, Deep learning for identifying radiogenomic associations in breast cancer, Comput. Biol. Med., 109, 85, 10.1016/j.compbiomed.2019.04.018 Liu, 2019, Image classification toward lung cancer recognition by learning deep quality model, J. Vis. Commun. Image Represent., 63, 102570, 10.1016/j.jvcir.2019.06.012 Guo, 2019, Identification of cancer subtypes by integrating multiple types of transcriptomics data with deep learning in breast cancer, Neurocomputing, 324, 20, 10.1016/j.neucom.2018.03.072 Liao, 2019, Multi-task deep convolutional neural network for cancer diagnosis, Neurocomputing, 348, 66, 10.1016/j.neucom.2018.06.084 Liu, 2019, Deep reinforcement learning with its application for lung cancer detection in medical Internet of Things, Future Gener. Comput. Syst., 97, 1, 10.1016/j.future.2019.02.068 Aresta, 2019, iW-Net: an automatic and minimalistic interactive lung nodule segmentation deep network, Sci. Rep., 9, 10.1038/s41598-019-48004-8 Shakeel, 2019, Lung cancer detection from CT image using improved profuse clustering and deep learning instantaneously trained neural networks, Measurement: J. Int. Measur. Confed., 145, 702, 10.1016/j.measurement.2019.05.027 Nasrullah, 2019, Automated lung nodule detection and classification using deep learning combined with multiple strategies, Sensors, 19, 10.3390/s19173722 Shen, 2019, An interpretable deep hierarchical semantic convolutional neural network for lung nodule malignancy classification, Expert Syst. Appl., 128, 84, 10.1016/j.eswa.2019.01.048 Khan, 2019, A novel deep learning based framework for the detection and classification of breast cancer using transfer learning, Pattern Recognit. Lett., 125, 1, 10.1016/j.patrec.2019.03.022 Pan, 2020, Multi-task deep learning for fine-grained classification/grading in breast cancer histopathological images, Stud. Comput. Intell., 810, 85, 10.1007/978-3-030-04946-1_10 Kumar, 2020, Deep feature learning for histopathological image classification of canine mammary tumors and human breast cancer, Inf. Sci., 508, 405, 10.1016/j.ins.2019.08.072 Zainudin, 2020, Deep layer CNN architecture for breast cancer histopathology image detection, Adv. Intell. Syst. Comput., 921, 43, 10.1007/978-3-030-14118-9_5 Shen, 2019, Deep learning to improve breast cancer detection on screening mammography, Sci. Rep., 9, 12495, 10.1038/s41598-019-48995-4 Turkki, 2019, Breast cancer outcome prediction with tumour tissue images and machine learning, Breast Canc. Res. Treat., 177, 41, 10.1007/s10549-019-05281-1 Watanabe, 2019, Improved cancer detection using artificial intelligence: a retrospective evaluation of missed cancers on mammography, J. Digit. Imaging, 32, 625, 10.1007/s10278-019-00192-5 Gomm, 2000 ScienceDirect Bray, 2018, Global cancer statistics 2018: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries, CA Cancer J Clin. Nov, 68, 394, 10.3322/caac.21492 Coccia, 2016, Problem-driven innovations in drug discovery: co-evolution of the patterns of radical innovation with the evolution of problems, Health Pol. Technol., 5, 143, 10.1016/j.hlpt.2016.02.003 Coccia, 2014, Path-breaking target therapies for lung cancer and a far-sighted health policy to support clinical and cost effectiveness, Health Pol. Technol., 1, 74, 10.1016/j.hlpt.2013.09.007 Coccia, 2012, Evolutionary growth of knowledge in path-breaking targeted therapies for lung cancer: radical innovations and structure of the new technological paradigm, Int. J. Behav. Healthc. Res., 3, 273, 10.1504/IJBHR.2012.051406 Dempke, 2010, Targeted therapies for non-small cell lung cancer, Lung Cancer, 67, 257, 10.1016/j.lungcan.2009.10.012 Coccia, 2012, Driving forces of technological change in medicine: radical innovations induced by side effects and their impact on society and healthcare, Technol. Soc., 34, 271, 10.1016/j.techsoc.2012.06.002 Coccia, 2015, Technological paradigms and trajectories as determinants of the R&D corporate change in drug discovery industry, Int. J. Knowl. Learn., 10, 29, 10.1504/IJKL.2015.071052 Coccia, 2015, The Nexus between technological performances of countries and incidence of cancers in society, Technol. Soc., 42, 61, 10.1016/j.techsoc.2015.02.003 Coccia, 2015, General sources of general purpose technologies in complex societies: theory of global leadership-driven innovation, warfare and human development, Technol. Soc., 42, 199, 10.1016/j.techsoc.2015.05.008 Coccia, 2018, General properties of the evolution of research fields: a scientometric study of human microbiome, evolutionary robotics and astrobiology, Scientometrics, 117, 1265, 10.1007/s11192-018-2902-8 Coccia, 2018, Optimization in R&D intensity and tax on corporate profits for supporting labor productivity of nations, J. Technol. Transf., 43, 792, 10.1007/s10961-017-9572-1 Coccia, 2018, A theory of the general causes of long waves: war, general purpose technologies, and economic change, Technol. Forecast. Soc. Chang., 128, 287, 10.1016/j.techfore.2017.11.013 Khosravi, 2018, Deep convolutional neural networks enable discrimination of heterogeneous digital pathology images, EBioMedicine, 27, 317, 10.1016/j.ebiom.2017.12.026 Coudray, 2018, Classification and mutation prediction from non–small cell lung cancer histopathology images using deep learning, Nat. Med., 24, 1559, 10.1038/s41591-018-0177-5 Russakovsky, 2015, ImageNet large scale visual recognition challenge, J. Int. J. Comput. Vision, 115, 211, 10.1007/s11263-015-0816-y Nogueira-Rodríguez, 2020, Deep learning techniques for real time computer-aided diagnosis in colorectal cancer, Adv. Intell. Syst. Comput., 1004, 209, 10.1007/978-3-030-23946-6_27 Menegotto, 2020, Computer-aided hepatocarcinoma diagnosis using multimodal deep learning, Adv. Intell. Syst. Comput., 1006, 3, 10.1007/978-3-030-24097-4_1 Swiderska-Chadaj, 2019, Learning to detect lymphocytes in immunohistochemistry with deep learning, Med. Image Anal., 58, 101547, 10.1016/j.media.2019.101547 Wang, 2019, RMDL: recalibrated multi-instance deep learning for whole slide gastric image classification, Med. Image Anal., 58, 10.1016/j.media.2019.101549 Kim, 2019, Deep learning-based survival prediction of oral cancer patients, Sci. Rep., 9, 6994, 10.1038/s41598-019-43372-7 Esteva, 2016, Dermatologist-level classification of skin cancer with deep neural networks, Nature, 542, 115, 10.1038/nature21056 Yu, 2016, Predicting non-small cell lung cancer prognosis by fully automated microscopic pathology image features, Nat. Commun., 7, 12474, 10.1038/ncomms12474 Jha, 2016, Innovations in health care delivery. Adapting to artificial intelligence radiologists and pathologists as information specialists, J. Am. Med. Assoc., 316, 2353, 10.1001/jama.2016.17438 Chagpar, 2019, Factors associated with breast cancer mortality-per-incident case in low-to-middle income countries (LMICs), J. Clin. Oncol., 37 Coccia, 2013, The effect of country wealth on incidence of breast cancer, Breast Canc. Res. Treat., 141, 225, 10.1007/s10549-013-2683-y Ehteshami Bejnordi, 2017, Diagnostic assessment of deep learning algorithms for detection of lymph node metastases in women with breast cancer, J. Am. Med. Assoc., 316, 2402 Lamy, 2019, Explainable artificial intelligence for breast cancer: a visual case-based reasoning approach, Artif. Intell. Med., 94, 42, 10.1016/j.artmed.2019.01.001 Gartner, 2018 Gartner, 2019 Dorn, 2015, Digital health: hope, hype, and Amara's law, Gastroenterology, 149, 516, 10.1053/j.gastro.2015.07.024 Coccia, 2015, Path-breaking directions of nanotechnology-based chemotherapy and molecular cancer therapy, Technol. Forecast. Soc. Chang., 94, 155, 10.1016/j.techfore.2014.09.007 Coccia, 2017, Sources of technological innovation: radical and incremental innovation problem-driven to support competitive advantage of firms, Technol. Anal. Strateg. Manag., 29, 1048, 10.1080/09537325.2016.1268682 Golden, 2017, Deep learning algorithms for detection of lymph node metastases from breast cancer: helping artificial intelligence be seen, J. Am. Med. Assoc., 318, 2184, 10.1001/jama.2017.14580 Ambrosini, 2008, Computer-aided detection of metastatic brain tumors using automated 3-D template matching, Proc. Intl. Soc. Mag. Reson. Med., 31, 85 Fetit, 2015, Three-dimensional textural features of conventional MRI improve diagnostic classification of childhood brain tumours, NMR Biomed., 28, 1174, 10.1002/nbm.3353 Maddox, 2019, Questions for artificial intelligence in health care, J. Am. Med. Assoc., 321, 31, 10.1001/jama.2018.18932 van Ginneken, 2011, Computer-aided diagnosis: how to move from the laboratory to the clinic, Radiology, 61, 719, 10.1148/radiol.11091710 Amara, 1984, New directions for futures research: setting the stage, Futures, 36, 43 Coccia, 2019, The theory of technological parasitism for the measurement of the evolution of technology and technological forecasting, Technol. Forecast. Soc. Chang., 141, 289, 10.1016/j.techfore.2018.12.012 Coccia, 2019, Why do nations produce science advances and new technology?, Technol. Soc., 10.1016/j.techsoc.2019.03.007 Coccia, 2019, The role of superpowers in conflict development and resolutions Coccia, 2019, A Theory of classification and evolution of technologies within a Generalized Darwinism, Technol. Anal. Strateg. Manag., 31, 517, 10.1080/09537325.2018.1523385 Coccia, 2016, Evolution and convergence of the patterns of international scientific collaboration, Proc. Natl. Acad. Sci. U. S. A, 113, 2057, 10.1073/pnas.1510820113 Wright, 1997, Towards a more historical approach to technological change, Econ. J., 107, 1560, 10.1111/j.1468-0297.1997.tb00066.x Coccia, 2019, Comparative theories of the evolution of technology, In: Farazmand A. (Eds) Global Encyclopedia of Public Administration, Public Policy, and Governance. Springer, Cham.