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Table representation of search results timeline featuring number of search results per year.

Year Number of Results
1958 1
1962 4
1964 1
1989 1
1990 4
1991 63
1992 161
1993 274
1994 322
1995 394
1996 465
1997 480
1998 493
1999 563
2000 621
2001 689
2002 677
2003 831
2004 1114
2005 1166
2006 1179
2007 1237
2008 1359
2009 1307
2010 1184
2011 1214
2012 1166
2013 1154
2014 1171
2015 1263
2016 1262
2017 1638
2018 2915
2019 1998
2020 36
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25,530 results
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Page 1
Artificial intelligence in retina
Schmidt-Erfurth U, et al. Prog Retin Eye Res 2018 - Review. PMID 30076935 Free article.
Convolutional neural networks thereby mimic the path of the human brain for object recognition through learning of pathological features from training sets, supervised ML, or even extrapolation from patterns recognized independently, unsupervised ML. ...
Convolutional neural networks thereby mimic the path of the human brain for object recognition through learning of pathologica …
Medical Image Analysis using Convolutional Neural Networks: A Review.
Anwar SM, et al. J Med Syst 2018 - Review. PMID 30298337
Deep learning is successfully used as a tool for machine learning, where a neural network is capable of automatically learning features. ...Among deep learning techniques, deep convolutional networks are actively used for the purpose of medical image analysis. This includes application areas such as segmentation, abnormality detection, disease classification, computer aided diagnosis and retrieval. ...
Deep learning is successfully used as a tool for machine learning, where a neural network is capable of automatically learning featur …
Convolutional Neural Networks for Radiologic Images: A Radiologist's Guide.
Soffer S, et al. Radiology 2019 - Review. PMID 30694159
In addition, the article details the results of a survey of the application of deep learning-specifically, the application of convolutional neural networks-to radiologic imaging that was focused on the following five major system organs: chest, breast, brain, musculoskeletal system, and abdomen and pelvis. ...This article may be used as a guide for radiologists planning research in the field of radiologic image analysis using convolutional neural networks....
In addition, the article details the results of a survey of the application of deep learning-specifically, the application of convolutional …
Artificial intelligence in medical imaging of the liver
Zhou LQ, et al. World J Gastroenterol 2019 - Review. PMID 30783371 Free PMC article.
This article illustrates basic technical knowledge about AI, including traditional machine learning and deep learning algorithms, especially convolutional neural networks, and their clinical application in the medical imaging of liver diseases, such as detecting and evaluating focal liver lesions, facilitating treatment, and predicting liver treatment response. ...
This article illustrates basic technical knowledge about AI, including traditional machine learning and deep learning algorithms, especially …
An overview of neural networks for drug discovery and the inputs used.
Xu Y, et al. Expert Opin Drug Discov 2018 - Review. PMID 30449189
Introduction: Artificial intelligence systems based on neural networks (NNs) find rules for drug discovery according to training molecules, but first, the molecules need to be represented in certain ways. Molecular descriptors and fingerprints have been used as inputs for artificial neural networks (ANNs) for a long time, while other ways for describing molecules are used only for storing and presenting molecules. ...
Introduction: Artificial intelligence systems based on neural networks (NNs) find rules for drug discovery according to traini …
Convolutional Neural Networks for ATC Classification.
Lumini A and Nanni L. Curr Pharm Des 2018 - Review. PMID 30417778
Finally, a convolutional neural network (CNN) is trained and used as a feature extractor. Two general purpose classifiers designed for multi-label classification are trained using the deep learned features and resulting scores are fused by the average rule. ...
Finally, a convolutional neural network (CNN) is trained and used as a feature extractor. Two general purpose classifiers designed fo …
Artificial intelligence in fracture detection: transfer learning from deep convolutional neural networks.
Kim DH and MacKinnon T. Clin Radiol 2018. PMID 29269036
AIM: To identify the extent to which transfer learning from deep convolutional neural networks (CNNs), pre-trained on non-medical images, can be used for automated fracture detection on plain radiographs. ...
AIM: To identify the extent to which transfer learning from deep convolutional neural networks (CNNs), pre-trained on non-medi …
Using deep learning to model the hierarchical structure and function of a cell
Ma J, et al. Nat Methods 2018. PMID 29505029 Free PMC article.
Although artificial neural networks are powerful classifiers, their internal structures are hard to interpret. In the life sciences, extensive knowledge of cell biology provides an opportunity to design visible neural networks (VNNs) that couple the model's inner workings to those of real systems. ...
Although artificial neural networks are powerful classifiers, their internal structures are hard to interpret. In the life sci …
Deep learning: new computational modelling techniques for genomics
Eraslan G, et al. Nat Rev Genet 2019 - Review. PMID 30971806
By effectively leveraging large data sets, deep learning has transformed fields such as computer vision and natural language processing. ...
By effectively leveraging large data sets, deep learning has transformed fields such as computer vision and natural language processi …
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