基于深度学习的医学图像处理方法综述

Yinglei Song, Mohammad N.A. Rana, Junfeng Qu, Chunmei Liu
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引用次数: 3

摘要

近年来,基于深度学习的方法已成为医学图像准确分析的重要途径。本文对医学图像处理中最重要的基于深度学习的方法进行了全面的综述。总结和调查了近五年来所作的一些重要贡献。具体来说,本文综述了基于深度学习的图像分割、图像分类、配准、目标检测等重要问题的算法。此外,在调查的最后,概述了该领域目前存在的挑战和未来研究的潜在方向。
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A Survey of Deep Learning Based Methods in Medical Image Processing
Recently, deep learning based methods have become an important approach to the accurate analysis of medical images. This paper provides a comprehensive survey of the most important deep learning based methods that have been developed for medical image processing. A number of important contributions made in last five years are summarized and surveyed. Specifically, deep learning based algorithms developed for image segmentation, image classification, registration, object detection and other important problems are reviewed. In addition, an overview of challenges that currently exist in the field and potential directions for future research is provided in the end of the survey.
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来源期刊
CiteScore
1.70
自引率
0.00%
发文量
18
审稿时长
>12 weeks
期刊介绍: In recent years a breakthrough has occurred in our understanding of the molecular pathomechanisms of human diseases whereby most of our diseases are related to intra and intercellular communication disorders. The concept of signal transduction therapy has got into the front line of modern drug research, and a multidisciplinary approach is being used to identify and treat signaling disorders. The journal publishes timely in-depth reviews, research article and drug clinical trial studies in the field of signal transduction therapy. Thematic issues are also published to cover selected areas of signal transduction therapy. Coverage of the field includes genomics, proteomics, medicinal chemistry and the relevant diseases involved in signaling e.g. cancer, neurodegenerative and inflammatory diseases. Current Signal Transduction Therapy is an essential journal for all involved in drug design and discovery.
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