Survey on deep learning for pulmonary medical imaging.

Abstract:

:As a promising method in artificial intelligence, deep learning has been proven successful in several domains ranging from acoustics and images to natural language processing. With medical imaging becoming an important part of disease screening and diagnosis, deep learning-based approaches have emerged as powerful techniques in medical image areas. In this process, feature representations are learned directly and automatically from data, leading to remarkable breakthroughs in the medical field. Deep learning has been widely applied in medical imaging for improved image analysis. This paper reviews the major deep learning techniques in this time of rapid evolution and summarizes some of its key contributions and state-of-the-art outcomes. The topics include classification, detection, and segmentation tasks on medical image analysis with respect to pulmonary medical images, datasets, and benchmarks. A comprehensive overview of these methods implemented on various lung diseases consisting of pulmonary nodule diseases, pulmonary embolism, pneumonia, and interstitial lung disease is also provided. Lastly, the application of deep learning techniques to the medical image and an analysis of their future challenges and potential directions are discussed.

journal_name

Front Med

journal_title

Frontiers of medicine

authors

Ma J,Song Y,Tian X,Hua Y,Zhang R,Wu J

doi

10.1007/s11684-019-0726-4

subject

Has Abstract

pub_date

2020-08-01 00:00:00

pages

450-469

issue

4

eissn

2095-0217

issn

2095-0225

pii

10.1007/s11684-019-0726-4

journal_volume

14

pub_type

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