Showing 20 of 2,239 academic articles
Cameron J. Buckner • Philosophy Compass
(2019)
Abstract Deep learning is currently the most prominent and widely successful method in artificial intelligence. Despite having played an active role in earlier artificial intelligence and neural network research, philosophers have been larg...
OPENALEX
DOI: https://doi.org/10.1111/phc3.12625
Changchun Yang, Hengrong Lan, Feng Gao, Fei Gao • Photoacoustics
(2020)
Machine learning has been developed dramatically and witnessed a lot of applications in various fields over the past few years. This boom originated in 2009, when a new model emerged, that is, the deep artificial neural network, which began...
OPENALEX
cc-by-nc-nd
DOI: https://doi.org/10.1016/j.pacs.2020.100215
PMC: 7779783
• De Gruyter eBooks
(2020)
This book focuses on the fundamentals of deep learning along with reporting on the current state-of-art research on deep learning. In addition, it provides an insight of deep neural networks in action with illustrative coding examples. Deep...
OPENALEX
DOI: https://doi.org/10.1515/9783110670905
Navin Kumar Manaswi • Apress eBooks
(2018)
This book helps you to ramp up your practical know-how in a short period of time and focuses you on the domain, models, and algorithms required for deep learning applications. Deep Learning with Applications Using Python covers topics such ...
OPENALEX
DOI: https://doi.org/10.1007/978-1-4842-3516-4
Heang‐Ping Chan, Lubomir M. Hadjiiski, Ravi K. Samala • Medical Physics
(2020)
Computer-aided diagnosis (CAD) has been a major field of research for the past few decades. CAD uses machine learning methods to analyze imaging and/or nonimaging patient data and makes assessment of the patient's condition, which can then ...
OPENALEX
Open Access
DOI: https://doi.org/10.1002/mp.13764
PMC: 7293164
Jiao Li, Xiaojuan Jing, Qin Zhang, Xiaoxiang Wang et al. • BMC Medical Informatics and Decision Making
(2025)
Abstract Background Subepithelial lesions (SELs) present significant diagnostic challenges in gastrointestinal endoscopy, particularly in differentiating malignant types, such as gastrointestinal stromal tumors (GISTs) and neuroendocrine tu...
DOAJ
Open Access (DOAJ)
DOI: 10.1186/s12911-025-03147-9
Synho Do, Kyoung Doo Song, Joo Won Chung • Korean Journal of Radiology
(2019)
Artificial intelligence has been applied to many industries, including medicine. Among the various techniques in artificial intelligence, deep learning has attained the highest popularity in medical imaging in recent years. Many articles on...
OPENALEX
cc-by-nc
DOI: https://doi.org/10.3348/kjr.2019.0312
PMC: 6960318
D. Guest, Kyle Stuart Cranmer, Daniel Whiteson • Annual Review of Nuclear and Particle Science
(2018)
Machine learning has played an important role in the analysis of high-energy physics data for decades. The emergence of deep learning in 2012 allowed for machine learning tools which could adeptly handle higher-dimensional and more complex ...
OPENALEX
Open Access
DOI: https://doi.org/10.1146/annurev-nucl-101917-021019
Mohd Arif Wani, Farooq Ahmad Bhat, Saduf Afzal, Asif Iqbal Khan • Studies in big data
(2019)
OPENALEX
DOI: https://doi.org/10.1007/978-981-13-6794-6
Ritika Wason • Cognitive Systems Research
(2018)
OPENALEX
DOI: https://doi.org/10.1016/j.cogsys.2018.08.023
Koichiro Yasaka, Hiroyuki Akai, Akira Kunimatsu, Shigeru Kiryu et al. • Japanese Journal of Radiology
(2018)
OPENALEX
DOI: https://doi.org/10.1007/s11604-018-0726-3
Shaveta Dargan, Munish Kumar, Maruthi Rohit Ayyagari, Gulshan Kumar • Archives of Computational Methods in Engineering
(2019)
OPENALEX
DOI: https://doi.org/10.1007/s11831-019-09344-w
Nitin Kumar Chauhan, Krishna Pal Singh • International Conference on Computing, Power and Communication Technologies (GUCON)
(2018)
In now days, deep learning has become a prominent and emerging research area in computer vision applications. Deep learning permits the multiple layers models for computation to learn representations of data by processing in their original ...
OPENALEX
DOI: https://doi.org/10.1109/gucon.2018.8675097
Berkman Sahiner, Aria X. Pezeshk, Lubomir M. Hadjiiski, Xiaosong Wang et al. • Medical Physics
(2018)
The goals of this review paper on deep learning (DL) in medical imaging and radiation therapy are to (a) summarize what has been achieved to date; (b) identify common and unique challenges, and strategies that researchers have taken to addr...
OPENALEX
Open Access
DOI: https://doi.org/10.1002/mp.13264
PMC: 9560030
Ehsan Rahimy • Current Opinion in Ophthalmology
(2018)
PURPOSE OF REVIEW: To describe the emerging applications of deep learning in ophthalmology. RECENT FINDINGS: Recent studies have shown that various deep learning models are capable of detecting and diagnosing various diseases afflicting the...
OPENALEX
DOI: https://doi.org/10.1097/icu.0000000000000470
Md Zahangir Alom, Tarek M. Taha, Chris Yakopcic, Stefan K.E. Westberg et al. • Electronics
(2019)
In recent years, deep learning has garnered tremendous success in a variety of application domains. This new field of machine learning has been growing rapidly and has been applied to most traditional application domains, as well as some ne...
OPENALEX
cc-by
DOI: https://doi.org/10.3390/electronics8030292
Lili Li, Shujuan Zhang, Bin Wang • IEEE Access
(2021)
Deep learning is a branch of artificial intelligence. In recent years, with the advantages of automatic learning and feature extraction, it has been widely concerned by academic and industrial circles. It has been widely used in image and v...
OPENALEX
cc-by
DOI: https://doi.org/10.1109/access.2021.3069646
Chayakrit Krittanawong, Kipp W. Johnson, Robert S. Rosenson, Zhen Wang et al. • European Heart Journal
(2019)
Deep learning (DL) is a branch of machine learning (ML) showing increasing promise in medicine, to assist in data classification, novel disease phenotyping and complex decision making. Deep learning is a form of ML typically implemented via...
OPENALEX
Open Access
DOI: https://doi.org/10.1093/eurheartj/ehz056
PMC: 6600129
June‐Goo Lee, Sanghoon Jun, Young-Won Cho, Hyunna Lee et al. • Korean Journal of Radiology
(2017)
The artificial neural network (ANN)-a machine learning technique inspired by the human neuronal synapse system-was introduced in the 1950s. However, the ANN was previously limited in its ability to solve actual problems, due to the vanishin...
OPENALEX
cc-by-nc
DOI: https://doi.org/10.3348/kjr.2017.18.4.570
PMC: 5447633
Yanming Yang, Xin Xia, David Lo, John Grundy • ACM Computing Surveys
(2021)
In 2006, Geoffrey Hinton proposed the concept of training “Deep Neural Networks (DNNs)” and an improved model training method to break the bottleneck of neural network development. More recently, the introduction of AlphaGo in 2016 demonstr...
OPENALEX
Open Access
DOI: https://doi.org/10.1145/3505243