Applications of deep learning for the analysis of medical data.

Hyun-Jong Jang, Kyung-Ok Cho
Author Information
  1. Hyun-Jong Jang: Department of Physiology, Department of Biomedicine & Health Sciences, Catholic Neuroscience Institute, College of Medicine, The Catholic University of Korea, Seoul, 06591, South Korea.
  2. Kyung-Ok Cho: Department of Pharmacology, Department of Biomedicine & Health Sciences, Catholic Neuroscience Institute, Institute of Aging and Metabolic Diseases, College of Medicine, The Catholic University of Korea, 222 Banpo-Daero, Seocho-Gu, Seoul, 06591, South Korea. kocho@catholic.ac.kr. ORCID

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.

Keywords

Grants

  1. HI15C2854/Korea Health Industry Development Institute

MeSH Term

Big Data
Computational Biology
Data Analysis
Datasets as Topic
Deep Learning
Drug Discovery
Electroencephalography
Humans
Radiographic Image Interpretation, Computer-Assisted
Seizures
Stomach

Word Cloud

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