| 医学图像深度学习处理方法的研究进展 |
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| 引用本文： | 佟超,韩勇,冯巍,李伟铭,陶丽新,郭秀花. 医学图像深度学习处理方法的研究进展[J]. 北京生物医学工程, 2021, 40(2): 198-202. DOI: 10.3969/j.issn.1002-3208.2021.02.014. |
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| 作者姓名： | 佟超 韩勇 冯巍 李伟铭 陶丽新 郭秀花 |
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| 作者单位： | 首都医科大学公共卫生学院,北京 100069;临床流行病学北京市重点实验室,北京 100069 |
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| 基金项目： | 国家自然科学基金;北京市教育委员会科技计划重点项目;"十三五"国家重点研发计划 |
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| 摘 要： | 由于医学图像数据爆炸式增长,传统依靠医生人工对医学图像进行分析诊断,不仅工作效率低下,工作量大,还容易误诊、漏诊。随着人工智能(artificial intelligence,AI)技术的发展与应用,机器学习(machine learning,ML),尤其是深度学习(deep learning,DL)在医学图像分析领域发挥着越来越重要的作用。本文对DL在医学图像自动分割和分类识别中的研究进展进行综述,为DL在解决医学图像分析诊断方面提供有益参考。
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| 关 键 词： | 医学图像 特征提取 自动分割 分类识别 深度学习 |
Research progress of deep learning processing methods for medical images |
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| TONG Chao,HAN Yong,FENG Wei,LI Weiming,TAO Lixin,GUO Xiuhua. Research progress of deep learning processing methods for medical images[J]. Beijing Biomedical Engineering, 2021, 40(2): 198-202. DOI: 10.3969/j.issn.1002-3208.2021.02.014. |
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| Authors: | TONG Chao HAN Yong FENG Wei LI Weiming TAO Lixin GUO Xiuhua |
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| Affiliation: | (School of Public Health,Capital Medical University,Beijing 100069;Beijing Key Laboratory of Clinical Epidemiology,Beijing 100069) |
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| Abstract: | Due to the explosive growth of medical image data,traditionally relying on doctors to analyze and diagnose medical images manually are not only low efficiency and heavy workload,but also easy to misdiagnoses and missed diagnoses.With the development and application of artificial intelligence technology,machine learning,especially deep learning,is playing an increasingly important role in the field of medical image analysis.This article reviews the research progress of deep learning in automatic segmentation and classification and recognition for medical images,providing a useful reference for deep learning in solving medical image analysis and diagnosis. |
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| Keywords: | medical image feature extraction automatic segmentation classification deep learning |
| 本文献已被 CNKI 维普 万方数据 等数据库收录！ |
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