Home »
Academic Articles & Research Papers
Showing 20 of 133 academic articles
Phillip M. Cheng, Emmanuel Montagnon, Rikiya Yamashita, Ian Pan et al. • Radiographics
(2021)
Deep learning is a class of machine learning methods that has been successful in computer vision. Unlike traditional machine learning methods that require hand-engineered feature extraction from input images, deep learning methods learn the...
OPENALEX
cc-by
DOI: https://doi.org/10.1148/rg.2021200210
Khoa Anh Tran, Olga Kondrashova, Andrew Peter Bradley, Elizabeth D. Williams et al. • Genome Medicine
(2021)
Deep learning is a subdiscipline of artificial intelligence that uses a machine learning technique called artificial neural networks to extract patterns and make predictions from large data sets. The increasing adoption of deep learning acr...
OPENALEX
cc-by
DOI: https://doi.org/10.1186/s13073-021-00968-x
PMC: 8477474
Alexander Selvikvåg Lundervold, Arvid Lundervold • Zeitschrift für Medizinische Physik
(2018)
What has happened in machine learning lately, and what does it mean for the future of medical image analysis? Machine learning has witnessed a tremendous amount of attention over the last few years. The current boom started around 2009 when...
OPENALEX
cc-by
DOI: https://doi.org/10.1016/j.zemedi.2018.11.002
Marek L. Borowiec, Rebecca B. Dikow, Paul B. Frandsen, Alexander J. McKeeken et al. • Methods in Ecology and Evolution
(2022)
Abstract Deep learning is driving recent advances behind many everyday technologies, including speech and image recognition, natural language processing and autonomous driving. It is also gaining popularity in biology, where it has been use...
OPENALEX
cc-by-nc
DOI: https://doi.org/10.1111/2041-210x.13901
Reza Shokri, Vitaly Shmatikov • ACM SIGSAC Conference on Computer and Communications Security (CCS)
(2015)
Deep learning based on artificial neural networks is a very popular approach to modeling, classifying, and recognizing complex data such as images, speech, and text. The unprecedented accuracy of deep learning methods has turned them into t...
OPENALEX
DOI: https://doi.org/10.1145/2810103.2813687
Jason Riordon, Dušan Sovilj, Scott Sanner, David A. Sinton et al. • Trends in biotechnology
(2018)
Advances in high-throughput and multiplexed microfluidics have rewarded biotechnology researchers with vast amounts of data but not necessarily the ability to analyze complex data effectively. Over the past few years, deep artificial neural...
OPENALEX
cc-by
DOI: https://doi.org/10.1016/j.tibtech.2018.08.005
Andreas Mayr, Günter Klambauer, Thomas Unterthiner, Sepp Hochreiter • Frontiers in Environmental Science
(2016)
The Tox21 Data Challenge has been the largest effort of the scientific community to compare computational methods for toxicity prediction. This challenge comprised 12,000 environmental chemicals and drugs which were measured for 12 differen...
OPENALEX
cc-by
DOI: https://doi.org/10.3389/fenvs.2015.00080
Anton S. Becker, Magda Marcon, Soleen Ghafoor, Moritz C. Wurnig et al. • Investigative Radiology
(2017)
OBJECTIVES: The aim of this study was to evaluate the diagnostic accuracy of a multipurpose image analysis software based on deep learning with artificial neural networks for the detection of breast cancer in an independent, dual-center mam...
OPENALEX
DOI: https://doi.org/10.1097/rli.0000000000000358
Rocio Vargas, Amir Hosein Mosavi, Ramon Ruiz • Preprints.org
(2018)
Deep learning is an emerging area of machine learning (ML) research. It comprises multiple hidden layers of artificial neural networks. The deep learn- ing methodology applies nonlinear transformations and model abstractions of high level i...
OPENALEX
cc-by
DOI: https://doi.org/10.20944/preprints201810.0218.v1
Chensi Cao, Feng Liu, Hai Tan, Deshou Song et al. • Genomics Proteomics & Bioinformatics
(2018)
Advances in biological and medical technologies have been providing us explosive volumes of biological and physiological data, such as medical images, electroencephalography, genomic and protein sequences. Learning from these data facilitat...
OPENALEX
cc-by
DOI: https://doi.org/10.1016/j.gpb.2017.07.003
PMC: 6000200
Hongming Chen, Ola Engkvist, Yinhai Wang, Marcus Olivecrona et al. • Drug Discovery Today
(2018)
Over the past decade, deep learning has achieved remarkable success in various artificial intelligence research areas. Evolved from the previous research on artificial neural networks, this technology has shown superior performance to other...
OPENALEX
cc-by
DOI: https://doi.org/10.1016/j.drudis.2018.01.039
Erik Gawehn, Jan A. Hiss, Gisbert Schneider • Molecular Informatics
(2015)
Artificial neural networks had their first heyday in molecular informatics and drug discovery approximately two decades ago. Currently, we are witnessing renewed interest in adapting advanced neural network architectures for pharmaceutical ...
OPENALEX
DOI: https://doi.org/10.1002/minf.201501008
Gabriel Chartrand, Phillip M. Cheng, Eugene Vorontsov, Michal Drozdzal et al. • Radiographics
(2017)
Deep learning is a class of machine learning methods that are gaining success and attracting interest in many domains, including computer vision, speech recognition, natural language processing, and playing games. Deep learning methods prod...
OPENALEX
DOI: https://doi.org/10.1148/rg.2017170077
Garrett B. Goh, Nathan O. Hodas, Abhinav Vishnu • Journal of Computational Chemistry
(2017)
The rise and fall of artificial neural networks is well documented in the scientific literature of both computer science and computational chemistry. Yet almost two decades later, we are now seeing a resurgence of interest in deep learning,...
OPENALEX
Open Access
DOI: https://doi.org/10.1002/jcc.24764
Daniele Ravì, Charence Wong, Fani Deligianni, Melissa Berthelot et al. • IEEE Journal of Biomedical and Health Informatics
(2016)
With a massive influx of multimodality data, the role of data analytics in health informatics has grown rapidly in the last decade. This has also prompted increasing interests in the generation of analytical, data driven models based on mac...
OPENALEX
cc-by
DOI: https://doi.org/10.1109/jbhi.2016.2636665
Grace W. Lindsay • Frontiers in Computational Neuroscience
(2020)
Attention is the important ability to flexibly control limited computational resources. It has been studied in conjunction with many other topics in neuroscience and psychology including awareness, vigilance, saliency, executive control, an...
OPENALEX
cc-by
DOI: https://doi.org/10.3389/fncom.2020.00029
PMC: 7177153
Nicholas M. Ball, Robert J. Brunner • International Journal of Modern Physics D
(2010)
We review the current state of data mining and machine learning in astronomy. Data Mining can have a somewhat mixed connotation from the point of view of a researcher in this field. If used correctly, it can be a powerful approach, holding ...
OPENALEX
Open Access
DOI: https://doi.org/10.1142/s0218271810017160
Jörn Lötsch, Alfred Ultsch • Pain
(2017)
1. Introduction Pain and pain chronification are incompletely understood and unresolved medical problems that continue to have a high prevalence.14 It has been accepted that pain is a complex phenomenon.2,32,72 Contemporary methods of compu...
OPENALEX
cc-by-nc-nd
DOI: https://doi.org/10.1097/j.pain.0000000000001118
PMC: 5895117
Yagang Zhang • InTech eBooks
(2010)
Machine learning techniques have the potential of alleviating the complexity of knowledge acquisition. This book presents today’s state and development tendencies of machine learning. It is a multi-author book. Taking into account the large...
OPENALEX
Open Access
DOI: https://doi.org/10.5772/217
K. W. DeGregory, Patrick K. Kuiper, Thomas DeSilvio, James D. Pleuss et al. • Obesity Reviews
(2018)
Rich sources of obesity-related data arising from sensors, smartphone apps, electronic medical health records and insurance data can bring new insights for understanding, preventing and treating obesity. For such large datasets, machine lea...
OPENALEX
Open Access
DOI: https://doi.org/10.1111/obr.12667
PMC: 8176949