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

Year Number of Results
1954 1
1957 1
1962 1
1965 2
1970 1
1971 1
1972 3
1974 2
1975 5
1976 3
1977 2
1978 2
1979 4
1980 8
1981 3
1982 6
1983 8
1984 5
1985 5
1986 5
1987 8
1988 4
1989 11
1990 14
1991 12
1992 12
1993 20
1994 12
1995 21
1996 27
1997 23
1998 24
1999 31
2000 47
2001 44
2002 51
2003 47
2004 57
2005 63
2006 85
2007 99
2008 105
2009 106
2010 107
2011 118
2012 146
2013 207
2014 265
2015 384
2016 643
2017 1352
2018 3081
2019 5606
2020 9301
2021 14474
2022 19600
2023 9405

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59,114 results

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Page 1
Deep-learning seismology.
Mousavi SM, Beroza GC. Mousavi SM, et al. Science. 2022 Aug 12;377(6607):eabm4470. doi: 10.1126/science.abm4470. Epub 2022 Aug 12. Science. 2022. PMID: 35951699 Review.
The availability of large-scale seismic datasets and the suitability of deep-learning techniques for seismic data processing have pushed deep learning to the forefront of fundamental, long-standing research investigations in seismology. ...Deep …
The availability of large-scale seismic datasets and the suitability of deep-learning techniques for seismic data processing h …
Emerging role of deep learning-based artificial intelligence in tumor pathology.
Jiang Y, Yang M, Wang S, Li X, Sun Y. Jiang Y, et al. Cancer Commun (Lond). 2020 Apr;40(4):154-166. doi: 10.1002/cac2.12012. Epub 2020 Apr 11. Cancer Commun (Lond). 2020. PMID: 32277744 Free PMC article. Review.
The development of digital pathology and progression of state-of-the-art algorithms for computer vision have led to increasing interest in the use of artificial intelligence (AI), especially deep learning (DL)-based AI, in tumor pathology. The DL-based algorithms ha …
The development of digital pathology and progression of state-of-the-art algorithms for computer vision have led to increasing interest in t …
Deep learning for electroencephalogram (EEG) classification tasks: a review.
Craik A, He Y, Contreras-Vidal JL. Craik A, et al. J Neural Eng. 2019 Jun;16(3):031001. doi: 10.1088/1741-2552/ab0ab5. Epub 2019 Feb 26. J Neural Eng. 2019. PMID: 30808014 Review.
(2) What input formulations have been used for training the deep networks? (3) Are there specific deep learning network structures suitable for specific types of tasks? ...SIGNIFICANCE: This review summarizes the current practices and performance outcomes in …
(2) What input formulations have been used for training the deep networks? (3) Are there specific deep learning network …
Multimodal deep learning for biomedical data fusion: a review.
Stahlschmidt SR, Ulfenborg B, Synnergren J. Stahlschmidt SR, et al. Brief Bioinform. 2022 Mar 10;23(2):bbab569. doi: 10.1093/bib/bbab569. Brief Bioinform. 2022. PMID: 35089332 Free PMC article. Review.
Biomedical data are becoming increasingly multimodal and thereby capture the underlying complex relationships among biological processes. Deep learning (DL)-based data fusion strategies are a popular approach for modeling these nonlinear relationships. ...Finally, w …
Biomedical data are becoming increasingly multimodal and thereby capture the underlying complex relationships among biological processes. …
Deep learning in clinical natural language processing: a methodical review.
Wu S, Roberts K, Datta S, Du J, Ji Z, Si Y, Soni S, Wang Q, Wei Q, Xiang Y, Zhao B, Xu H. Wu S, et al. J Am Med Inform Assoc. 2020 Mar 1;27(3):457-470. doi: 10.1093/jamia/ocz200. J Am Med Inform Assoc. 2020. PMID: 31794016 Free PMC article. Review.
OBJECTIVE: This article methodically reviews the literature on deep learning (DL) for natural language processing (NLP) in the clinical domain, providing quantitative analysis to answer 3 research questions concerning methods, scope, and context of current research. …
OBJECTIVE: This article methodically reviews the literature on deep learning (DL) for natural language processing (NLP) in the …
Deep Learning in Diverse Intelligent Sensor Based Systems.
Zhu Y, Wang M, Yin X, Zhang J, Meijering E, Hu J. Zhu Y, et al. Sensors (Basel). 2022 Dec 21;23(1):62. doi: 10.3390/s23010062. Sensors (Basel). 2022. PMID: 36616657 Free PMC article. Review.
With the rapid development of deep learning technology and its ever-increasing range of successful applications across diverse sensor systems, there is an urgent need to provide a comprehensive investigation of deep learning in this domain from a holis …
With the rapid development of deep learning technology and its ever-increasing range of successful applications across diverse …
Deep Learning in Medical Hyperspectral Images: A Review.
Cui R, Yu H, Xu T, Xing X, Cao X, Yan K, Chen J. Cui R, et al. Sensors (Basel). 2022 Dec 13;22(24):9790. doi: 10.3390/s22249790. Sensors (Basel). 2022. PMID: 36560157 Free PMC article. Review.
With the continuous progress of development, deep learning has made good progress in the analysis and recognition of images, which has also triggered some researchers to explore the area of combining deep learning with hyperspectral medical images and …
With the continuous progress of development, deep learning has made good progress in the analysis and recognition of images, w …
Explainable artificial intelligence (XAI) in deep learning-based medical image analysis.
van der Velden BHM, Kuijf HJ, Gilhuijs KGA, Viergever MA. van der Velden BHM, et al. Med Image Anal. 2022 Jul;79:102470. doi: 10.1016/j.media.2022.102470. Epub 2022 May 4. Med Image Anal. 2022. PMID: 35576821 Free article. Review.
With an increase in deep learning-based methods, the call for explainability of such methods grows, especially in high-stakes decision making areas such as medical image analysis. This survey presents an overview of explainable artificial intelligence (XAI) used in …
With an increase in deep learning-based methods, the call for explainability of such methods grows, especially in high-stakes …
A review of medical image data augmentation techniques for deep learning applications.
Chlap P, Min H, Vandenberg N, Dowling J, Holloway L, Haworth A. Chlap P, et al. J Med Imaging Radiat Oncol. 2021 Aug;65(5):545-563. doi: 10.1111/1754-9485.13261. Epub 2021 Jun 19. J Med Imaging Radiat Oncol. 2021. PMID: 34145766 Review.
Research in artificial intelligence for radiology and radiotherapy has recently become increasingly reliant on the use of deep learning-based algorithms. While the performance of the models which these algorithms produce can significantly outperform more traditional …
Research in artificial intelligence for radiology and radiotherapy has recently become increasingly reliant on the use of deep lea …
Deep learning methods for molecular representation and property prediction.
Li Z, Jiang M, Wang S, Zhang S. Li Z, et al. Drug Discov Today. 2022 Dec;27(12):103373. doi: 10.1016/j.drudis.2022.103373. Epub 2022 Sep 24. Drug Discov Today. 2022. PMID: 36167282 Review.
Effective molecular representation and accurate property prediction are crucial tasks in CADD workflows. In this review, we summarize contemporary applications of deep learning (DL) methods for molecular representation and property prediction. ...In addition, we dis …
Effective molecular representation and accurate property prediction are crucial tasks in CADD workflows. In this review, we summarize contem …
59,114 results
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