Literature DB >> 31140082

Applications of deep learning for the analysis of medical data.

Hyun-Jong Jang1, Kyung-Ok Cho2.   

Abstract

Over the past decade, deep learning has demonstrated superior performances in solving many problems in various fields of medicine compared with other machine learning methods. To understand how deep learning has surpassed traditional machine learning techniques, in this review, we briefly explore the basic learning algorithms underlying deep learning. In addition, the procedures for building deep learning-based classifiers for seizure electroencephalograms and gastric tissue slides are described as examples to demonstrate the simplicity and effectiveness of deep learning applications. Finally, we review the clinical applications of deep learning in radiology, pathology, and drug discovery, where deep learning has been actively adopted. Considering the great advantages of deep learning techniques, deep learning will be increasingly and widely utilized in a wide variety of different areas in medicine in the coming decades.

Entities:  

Keywords:  Artificial intelligence; Deep neural networks; Drug discovery; Medical image analysis

Mesh:

Year:  2019        PMID: 31140082     DOI: 10.1007/s12272-019-01162-9

Source DB:  PubMed          Journal:  Arch Pharm Res        ISSN: 0253-6269            Impact factor:   4.946


  13 in total

1.  Multi-Class Classification of Medical Data Based on Neural Network Pruning and Information-Entropy Measures.

Authors:  Máximo Eduardo Sánchez-Gutiérrez; Pedro Pablo González-Pérez
Journal:  Entropy (Basel)       Date:  2022-01-27       Impact factor: 2.524

2.  Feasibility of fully automated classification of whole slide images based on deep learning.

Authors:  Kyung-Ok Cho; Sung Hak Lee; Hyun-Jong Jang
Journal:  Korean J Physiol Pharmacol       Date:  2020-12-20       Impact factor: 2.016

Review 3.  Artificial intelligence in oncology.

Authors:  Hideyuki Shimizu; Keiichi I Nakayama
Journal:  Cancer Sci       Date:  2020-03-21       Impact factor: 6.716

4.  Analyzing Surgical Treatment of Intestinal Obstruction in Children with Artificial Intelligence.

Authors:  Wang-Ren Qiu; Gang Chen; Jin Wu; Jun Lei; Lei Xu; Shou-Hua Zhang
Journal:  Comput Math Methods Med       Date:  2021-01-11       Impact factor: 2.238

5.  Facial UV photo imaging for skin pigmentation assessment using conditional generative adversarial networks.

Authors:  Kaname Kojima; Kosuke Shido; Gen Tamiya; Kenshi Yamasaki; Kengo Kinoshita; Setsuya Aiba
Journal:  Sci Rep       Date:  2021-01-13       Impact factor: 4.379

Review 6.  Artificial intelligence and computational pathology.

Authors:  Miao Cui; David Y Zhang
Journal:  Lab Invest       Date:  2021-01-16       Impact factor: 5.662

7.  Deep learning to classify arteriovenous access aneurysms in hemodialysis patients.

Authors:  Hanjie Zhang; Dean Preddie; Warren Krackov; Murat Sor; Peter Waguespack; Zuwen Kuang; Xiaoling Ye; Peter Kotanko
Journal:  Clin Kidney J       Date:  2021-12-16

8.  Molecular Insights from Conformational Ensembles via Machine Learning.

Authors:  Oliver Fleetwood; Marina A Kasimova; Annie M Westerlund; Lucie Delemotte
Journal:  Biophys J       Date:  2019-12-21       Impact factor: 4.033

9.  A Novel Multiple-Cue Observational Clinical Scale for Functional Evaluation of Gait After Stroke - The Stroke Mobility Score (SMS).

Authors:  Dominik Raab; Brigitta Diószeghy-Léránt; Meret Wünnemann; Christina Zumfelde; Elena Cramer; Alina Rühlemann; Johanna Wagener; Silke Gegenbauer; Francisco Geu Flores; Marcus Jäger; Dörte Zietz; Harald Hefter; Andres Kecskemethy; Mario Siebler
Journal:  Med Sci Monit       Date:  2020-09-15

Review 10.  Artificial intelligence enabled applications in kidney disease.

Authors:  Sheetal Chaudhuri; Andrew Long; Hanjie Zhang; Caitlin Monaghan; John W Larkin; Peter Kotanko; Shashi Kalaskar; Jeroen P Kooman; Frank M van der Sande; Franklin W Maddux; Len A Usvyat
Journal:  Semin Dial       Date:  2020-09-13       Impact factor: 3.455

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