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Deep learning: A philosophical introduction

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

Review of deep learning for photoacoustic imaging

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

Deep Learning

• 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

Deep Learning with Applications Using Python

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

Computer‐aided diagnosis in the era of deep learning

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

Multimodal artificial intelligence for subepithelial lesion classification and characterization: a multicenter comparative study (with video)

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

Basics of Deep Learning: A Radiologist's Guide to Understanding Published Radiology Articles on Deep Learning

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

Deep Learning and Its Application to LHC Physics

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

A Review on Conventional Machine Learning vs Deep Learning

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

Deep learning in medical imaging and radiation therapy

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

Deep learning applications in ophthalmology

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

A State-of-the-Art Survey on Deep Learning Theory and Architectures

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

Deep learning for cardiovascular medicine: a practical primer

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

Deep Learning in Medical Imaging: General Overview

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

A Survey on Deep Learning for Software Engineering

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