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Medical Deep Learning – A systematic Meta-Review [article]

Jan Egger, Christina Gsaxner, Antonio Pepe, Jianning Li
2020 arXiv   pre-print
Nevertheless, several review and survey articles about medical deep learning have been presented within the last years.  ...  Deep learning had a remarkable impact in different scientific disciplines during the last years.  ...  [54] perform a review of recent advancements on cardiac arrhythmia detection using deep learning approaches.  ... 
arXiv:2010.14881v4 fatcat:56nrzawncnaopcpuzlzac5ceoy

A Review on Methods and Applications in Multimodal Deep Learning [article]

Jabeen Summaira, Xi Li, Amin Muhammad Shoib, Jabbar Abdul
2022 arXiv   pre-print
Deep Learning has implemented a wide range of applications and has become increasingly popular in recent years.  ...  Detailed analysis of the baseline approaches and an in-depth study of recent advancements during the last five years (2017 to 2021) in multimodal deep learning applications has been provided.  ...  This section discusses recent research trends and advancements of Deep Learning TTS.  ... 
arXiv:2202.09195v1 fatcat:wwxrmrwmerfabbenleylwmmj7y

Deep learning in robotics: a review of recent research

Harry A. Pierson, Michael S. Gashler
2017 Advanced Robotics  
It is intended to communicate recent advances to the wider robotics community and inspire additional interest in and application of deep learning in robotics.  ...  This review discusses the applications, benefits, and limitations of deep learning vis-à-vis physical robotic systems, using contemporary research as exemplars.  ...  A primer on deep learning is followed by a discussion of how common DNN structures are used in robotics and in examples from the recent literature.  ... 
doi:10.1080/01691864.2017.1365009 fatcat:wmp3fphajnfsnev2omty3uswjq

3D Deep Learning on Medical Images: A Review [article]

Satya P. Singh, Lipo Wang, Sukrit Gupta, Haveesh Goli, Parasuraman Padmanabhan, Balázs Gulyás
2020 arXiv   pre-print
The rapid advancements in machine learning, graphics processing technologies and the availability of medical imaging data have led to a rapid increase in the use of deep learning models in the medical  ...  We conclude by discussing the challenges associated with the use of 3D CNNs in the medical imaging domain (and the use of deep learning models in general) and possible future trends in the field.  ...  However, with the recent advancements in neural network architectures, data augmentation techniques and high-end GPUs, it is becoming possible to analyze the volumetric medical data using 3D deep learning  ... 
arXiv:2004.00218v4 fatcat:iucszcjffnbwbbzc4zzqpbvahy

Deep Learning in Multimodal Remote Sensing Data Fusion: A Comprehensive Review [article]

Jiaxin Li, Danfeng Hong, Lianru Gao, Jing Yao, Ke Zheng, Bing Zhang, Jocelyn Chanussot
2022 arXiv   pre-print
Deep learning (DL), as a cutting-edge technology, has witnessed remarkable breakthroughs in numerous computer vision tasks owing to its impressive ability in data representation and reconstruction.  ...  Some prevalent sub-fields in the multimodal RS data fusion are then reviewed in terms of the to-be-fused data modalities, i.e., spatiospectral, spatiotemporal, light detection and ranging-optical, synthetic  ...  Acknowledgements This work was supported by the National Natural Science Foundation of China [62161160336, 42030111]; MIAI@Grenoble Alpes [ANR-19-P3IA-0003]; and the AXA Research Fund.  ... 
arXiv:2205.01380v1 fatcat:5btxnj5e5rf2xn65iofrh4epbu

Deep Learning for Medical Image Registration: A Comprehensive Review [article]

Subrato Bharati, M. Rubaiyat Hossain Mondal, Prajoy Podder, V. B. Surya Prasath
2022 arXiv   pre-print
In recent years, there has been a tremendous surge in the development of deep learning (DL)-based medical image registration models.  ...  This review focuses on monomodal and multimodal registration and associated imaging, for instance, X-ray, CT scan, ultrasound, and MRI.  ...  Conclusions Recent efforts using DL for medical image registration have been reviewed in this article.  ... 
arXiv:2204.11341v1 fatcat:n6yacnk3ffdallbeirsgqpj274

Fair Machine Learning in Healthcare: A Review [article]

Qizhang Feng, Mengnan Du, Na Zou, Xia Hu
2022 arXiv   pre-print
However, the intersection of machine learning for healthcare and fairness in machine learning remains understudied.  ...  In this review, we build the bridge by exposing fairness problems, summarizing possible biases, sorting out mitigation methods and pointing out challenges along with opportunities for the future.  ...  Conclusions In this survey report, we provide an overview of current advances in machine learning for fairness in healthcare.  ... 
arXiv:2206.14397v1 fatcat:33rsyb5hm5c2xedpi35ypv5usy

APPLICATION OF DEEP LEARNING IN HEALTH INFORMATICS: A REVIEW

Vinit Mehta, Noopur Shrivastava
2021 International Journal of Technical Research & Science  
Deep learning, a technique with its foundation in artificial neural networks, is emerging in recent years as a powerful tool for machine learning, promising to reform the future of artificial intelligence  ...  This paper presents a comprehensive review of research employing deep learning in health informatics, providing a critical analysis of the relative merit, and potential pitfalls of the technique as well  ...  INTRODUCTION Deep learning has become a new trend in machine learning since recent years. The background foundations of deep learning are well rooted in the classical neural network (NN) theory.  ... 
doi:10.30780/specialissue-icrdet-2021/002 fatcat:gsv76fv4qbe5fh5g3va5da2izu

Machine Learning in Knee Osteoarthritis: A Review

C. Kokkotis, S. Moustakidis, E. Papageorgiou, G. Giakas, D.E. Tsaopoulos
2020 Osteoarthritis and Cartilage Open  
Section 2 Machine Learning in a nutshell presents the terminology and definitions, 33 the types, tasks and models, which are used in the studies on which this review was based. 34 Section 3 Review of studies  ...  machine learning, deep learning and knee osteoarthritis were used.  ...  Hinton, Deep Boltzmann Machines, in Proceedings of Zhang, L., et al., A review on deep learning applications in prognostics and health Pedoia, V., et al., MRI and biomechanics multidimensional  ... 
doi:10.1016/j.ocarto.2020.100069 fatcat:gozdl73zu5hfhal37i3oi5wxri

Machine learning in handling disease outbreaks: a comprehensive review

Dianadewi Riswantini, Ekasari Nugraheni
2022 Bulletin of Electrical Engineering and Informatics  
This work aims to identify and analyze previous studies on machine learning applications in handling disease outbreaks.  ...  A huge amount of multimodal medical data was used by previous studies for prediction, forecasting, classification, or screening purposes to resolve many problems of diseases, including epidemiological  ...  ACKNOWLEDGEMENTS The authors wish to thank the members of the Information Retrieval Research Group at the Research Centre for Data and Information Sciences, National Agency of Research and Innovation (  ... 
doi:10.11591/eei.v11i4.3612 fatcat:tmhzunn72fc3rpalf7tr3jgoz4

3D Deep Learning on Medical Images: A Review

Satya P. Singh, Lipo Wang, Sukrit Gupta, Haveesh Goli, Parasuraman Padmanabhan, Balázs Gulyás
2020 Sensors  
The rapid advancements in machine learning, graphics processing technologies and the availability of medical imaging data have led to a rapid increase in the use of deep learning models in the medical  ...  We conclude by discussing the challenges associated with the use of 3D CNNs in the medical imaging domain (and the use of deep learning models in general) and possible future trends in the field.  ...  We observed a similar trend for the query 'deep learning + medical', albeit with few publications before 2015.  ... 
doi:10.3390/s20185097 pmid:32906819 pmcid:PMC7570704 fatcat:top2ambpizdzdpsqamz2xm643u

Multimodal Data Fusion in Learning Analytics: A Systematic Review

Su Mu, Meng Cui, Xiaodi Huang
2020 Sensors  
Our main findings from this review are (a) The data in MMLA are classified into digital data, physical data, physiological data, psychometric data, and environment data; (b) The learning indicators are  ...  For this purpose, we first present a conceptual model for reviewing these articles from three dimensions: data types, learning indicators, and data fusion.  ...  Finally, from the method level, the recent advancement of artificial intelligence enables MMLA. The use of multimodal data does not mean data integration.  ... 
doi:10.3390/s20236856 pmid:33266131 pmcid:PMC7729570 fatcat:lcnlxpiw5zcwjhrzc26c2zq6b4

Deep Learning in Medical Image Registration: A Review [article]

Yabo Fu, Yang Lei, Tonghe Wang, Walter J. Curran, Tian Liu, Xiaofeng Yang
2019 arXiv   pre-print
This paper presents a review of deep learning (DL) based medical image registration methods.  ...  Lastly, we analyzed the statistics of all the cited works from various aspects, revealing the popularity and future trend of development in medical image registration using deep learning.  ...  Acknowledgements This research is supported in part by the National Cancer Institute of the National Institutes of Health under Award Number R01CA215718, and Dunwoody Golf Club Prostate Cancer Research  ... 
arXiv:1912.12318v1 fatcat:kuvckosqd5hp7asg6dofhuiis4

Deep learning-based electroencephalography analysis: a systematic review

Yannick Roy, Hubert Banville, Isabela Albuquerque, Alexandre Gramfort, Tiago H Falk, Jocelyn Faubert
2019 Journal of Neural Engineering  
Recently, deep learning (DL) has shown great promise in helping make sense of EEG signals due to its capacity to learn good feature representations from raw data.  ...  Electroencephalography (EEG) is a complex signal and can require several years of training, as well as advanced signal processing and feature extraction methodologies to be correctly interpreted.  ...  Funding This work was supported by the Natural Sciences and Engineering Research Council of Canada (NSERC-RDC) for JF and YR (reference number: RDPJ 514052-17), NSERC research funds for JF, HB, IA and  ... 
doi:10.1088/1741-2552/ab260c pmid:31151119 fatcat:tgb2o34h2zbx7jft2d6bqbkvlu

Processing of Electronic Health Records using Deep Learning: A review [article]

Venet Osmani, Li Li, Matteo Danieletto, Benjamin Glicksberg, Joel Dudley, Oscar Mayora
2018 arXiv   pre-print
to this trend is also being made by recent advances on machine learning, specifically deep learning algorithms.  ...  Availability of large amount of clinical data is opening up new research avenues in a number of fields.  ...  medicine efficiency. 3.1.2 Advancing mental illness prediction using deep learning Another compelling use of EMR-based research is the prediction of future disease outcomes in a data-driven approach  ... 
arXiv:1804.01758v1 fatcat:op5hsclwrvflxcbhfhidwmodyu
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