Survey on deep learning for pulmonary medical imaging
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REVIEW
Survey on deep learning for pulmonary medical imaging Jiechao Ma1,*, Yang Song2,*, Xi Tian1, Yiting Hua1, Rongguo Zhang1, Jianlin Wu (
✉)3
1
InferVision, Beijing 100020, China; 2Dalian Municipal Central Hospital Affiliated to Dalian Medical University, Dalian 116033, China; Affiliated Zhongshan Hospital of Dalian University, Dalian 116001, China
3
© The Author(s) 2019. This article is published with open access at link.springer.com and journal.hep.com.cn 2019
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. Keywords
deep learning; neural networks; pulmonary medical image; survey
Introduction Deep learning covers a set of artificial intelligence methods that use many interconnected units to fulfill complex tasks. Deep learning algorithms can automatically learn representations from large amounts of data rather than the use of a set of pre-programmed instructions [1–3]. Radiology is a natural application field for deep learning because it relies mainly on extracting useful information from images, and the research in this field has rapidly developed [4]. With the aggravation of air pollution and the increasing number of smokers, respiratory diseases have become a serious threat to people’s life and health [5]. However, many of the early clinical manifestations of respiratory diseases are not evident, and some patients do not even feel any discomfort during the early stage. Hence, many patients miss the critical period of early treatment because they discover clinical symptoms at a later time. Therefore, public awareness of early detection and treatment is necessary for the prevention and treatment of lung diseases. In
Received July 18, 2019; accepted October 12, 2019 Correspondence: Jianlin Wu, [email protected] *
These authors contributed equally to this work.
medical imaging, the accurate diagno
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