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Showing 20 of 1,126 academic articles
Andrew J. Reader, Guillaume Corda-D’Incan, Abolfazl Mehranian, Casper O. da Costa-Luis et al. • IEEE Transactions on Radiation and Plasma Medical Sciences
(2020)
This article reviews the use of a subdiscipline of artificial intelligence (AI), deep learning, for the reconstruction of images in positron emission tomography (PET). Deep learning can be used either directly or as a component of conventio...
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Open Access
DOI: https://doi.org/10.1109/trpms.2020.3014786
Vishwa S. Parekh, Michael A. Jacobs • Expert Review of Precision Medicine and Drug Development
(2019)
INTRODUCTION: The radiological reading room is undergoing a paradigm shift to a symbiosis of computer science and radiology using artificial intelligence integrated with machine and deep learning with radiomics to better define tissue chara...
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DOI: https://doi.org/10.1080/23808993.2019.1585805
PMC: 6508888
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...
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DOI: https://doi.org/10.1186/s13073-021-00968-x
PMC: 8477474
Yudong Zhang, Lijia Deng, Hengde Zhu, Wei Wang et al. • Information Fusion
(2023)
Integrating artificial intelligence with food category recognition has been a field of interest for research for the past few decades. It is potentially one of the next steps in revolutionizing human interaction with food. The modern advent...
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DOI: https://doi.org/10.1016/j.inffus.2023.101859
Taiyu Zhu, Kezhi Li, Pau Herrero, Pantelis Georgiou • IEEE Journal of Biomedical and Health Informatics
(2020)
Diabetes is a chronic metabolic disorder that affects an estimated 463 million people worldwide. Aiming to improve the treatment of people with diabetes, digital health has been widely adopted in recent years and generated a huge amount of ...
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other-oa
DOI: https://doi.org/10.1109/jbhi.2020.3040225
Mufti Mahmud, M. Shamim Kaiser, Amir Hussain, Stefano Vassanelli • IEEE Transactions on Neural Networks and Learning Systems
(2018)
Rapid advances in hardware-based technologies during the past decades have opened up new possibilities for life scientists to gather multimodal data in various application domains, such as omics, bioimaging, medical imaging, and (brain/body...
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Open Access
DOI: https://doi.org/10.1109/tnnls.2018.2790388
M. A. Ganaie, Minghui Hu, A. K. Malik, M. Tanveer et al. • Engineering Applications of Artificial Intelligence
(2022)
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Open Access
DOI: https://doi.org/10.1016/j.engappai.2022.105151
Jae Joon Hwang, Yun‐Hoa Jung, Bong‐Hae Cho, Min-Suk Heo • Imaging Science in Dentistry
(2019)
PURPOSE: Artificial intelligence (AI), represented by deep learning, can be used for real-life problems and is applied across all sectors of society including medical and dental field. The purpose of this study is to review articles about d...
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DOI: https://doi.org/10.5624/isd.2019.49.1.1
PMC: 6444007
S. Suganyadevi, V. Seethalakshmi, K. Balasamy • International Journal of Multimedia Information Retrieval
(2021)
Ongoing improvements in AI, particularly concerning deep learning techniques, are assisting to identify, classify, and quantify patterns in clinical images. Deep learning is the quickest developing field in artificial intelligence and is ef...
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DOI: https://doi.org/10.1007/s13735-021-00218-1
PMC: 8417661
Massimiliano Luca, Gianni Barlacchi, Bruno Lepri, Luca Pappalardo • ACM Computing Surveys
(2021)
The study of human mobility is crucial due to its impact on several aspects of our society, such as disease spreading, urban planning, well-being, pollution, and more. The proliferation of digital mobility data, such as phone records, GPS t...
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Open Access
DOI: https://doi.org/10.1145/3485125
Christopher D. Manning • Computational Linguistics
(2015)
Deep Learning waves have lapped at the shores of computational linguistics for several years now, but 2015 seems like the year when the full force of the tsunami hit the major Natural Language Processing (NLP) conferences. However, some pun...
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Open Access
DOI: https://doi.org/10.1162/coli_a_00239
Jing Li, Aixin Sun, Jianglei Han, Chenliang Li • IEEE Transactions on Knowledge and Data Engineering
(2020)
Named entity recognition (NER) is the task to identify mentions of rigid designators from text belonging to predefined semantic types such as person, location, organization etc. NER always serves as the foundation for many natural language ...
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Open Access
DOI: https://doi.org/10.1109/tkde.2020.2981314
Gustau Camps‐Valls, Devis Tuia, Xiao Xiang Zhu, Markus Reichstein • Wiley eBooks
(2021)
Deep learning is a fundamental technique in modern Artificial Intelligence and is being applied to disciplines across the scientific spectrum; earth science is no exception. Yet, the link between deep learning and Earth sciences has only re...
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Open Access
DOI: https://doi.org/10.1002/9781119646181
Jordan Guerguiev, Timothy Lillicrap, Blake A. Richards • eLife
(2017)
Deep learning has led to significant advances in artificial intelligence, in part, by adopting strategies motivated by neurophysiology. However, it is unclear whether deep learning could occur in the real brain. Here, we show that a deep le...
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DOI: https://doi.org/10.7554/elife.22901
PMC: 5716677
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...
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DOI: https://doi.org/10.20944/preprints201810.0218.v1
Yutaka Matsuo, Yann LeCun, Maneesh Sahani, Doina Precup et al. • Neural Networks
(2022)
Deep learning (DL) and reinforcement learning (RL) methods seem to be a part of indispensable factors to achieve human-level or super-human AI systems. On the other hand, both DL and RL have strong connections with our brain functions and w...
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DOI: https://doi.org/10.1016/j.neunet.2022.03.037
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...
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DOI: https://doi.org/10.1016/j.drudis.2018.01.039
Blake A. Richards, Timothy Lillicrap, Philippe Beaudoin, Yoshua Bengio et al. • Nature Neuroscience
(2019)
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Open Access
DOI: https://doi.org/10.1038/s41593-019-0520-2
PMC: 7115933
Sylvain Christin, Éric Hervet, Nicolas Lecomte • Methods in Ecology and Evolution
(2019)
Abstract A lot of hype has recently been generated around deep learning, a group of artificial intelligence approaches able to break accuracy records in pattern recognition. Over the course of just a few years, deep learning revolutionized ...
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Open Access
DOI: https://doi.org/10.1111/2041-210x.13256
Neil Thompson, Kristjan H. Greenewald, Keeheon Lee, Gabriel F. Manso (2023)
Deep learning's recent history has been one of achievement: from triumphing over humans in the game of Go to world-leading performance in image classification, voice recognition, translation, and other tasks.But this progress has come with ...
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DOI: https://doi.org/10.21428/bf6fb269.1f033948