深度学习在医学图像分析中的研究进展。

IF 3.6 3区 计算机科学 Q2 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE
S Suganyadevi, V Seethalakshmi, K Balasamy
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引用次数: 77

摘要

人工智能的持续改进,特别是在深度学习技术方面,正在帮助识别、分类和量化临床图像中的模式。深度学习是人工智能中发展最快的领域,最近在包括医疗在内的许多领域得到了有效的应用。简要概述了在应用领域进行的研究:神经、脑、视网膜、肺炎、计算机病理学、胸部、心脏、乳房、骨骼、胃和肌肉骨骼。对于信息探索、知识部署和基于知识的预测,深度学习网络可以成功地应用于大数据。在医学图像处理方法和分析领域,本文介绍了深度学习的基本信息和最新方法。本文的主要目标是介绍医学图像处理的研究,以及定义和实施所确定和解决的关键指导方针。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

A review on deep learning in medical image analysis.

A review on deep learning in medical image analysis.

A review on deep learning in medical image analysis.

A review on deep learning in medical image analysis.

Ongoing improvements in AI, particularly concerning deep learning techniques, are assisting to identify, classify, and quantify patterns in clinical images. Deep learning is the quickest developing field in artificial intelligence and is effectively utilized lately in numerous areas, including medication. A brief outline is given on studies carried out on the region of application: neuro, brain, retinal, pneumonic, computerized pathology, bosom, heart, breast, bone, stomach, and musculoskeletal. For information exploration, knowledge deployment, and knowledge-based prediction, deep learning networks can be successfully applied to big data. In the field of medical image processing methods and analysis, fundamental information and state-of-the-art approaches with deep learning are presented in this paper. The primary goals of this paper are to present research on medical image processing as well as to define and implement the key guidelines that are identified and addressed.

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来源期刊
CiteScore
7.80
自引率
5.40%
发文量
36
期刊介绍: Aims and Scope The International Journal of Multimedia Information Retrieval (IJMIR) is a scholarly archival journal publishing original, peer-reviewed research contributions. Its editorial board strives to present the most important research results in areas within the field of multimedia information retrieval. Core areas include exploration, search, and mining in general collections of multimedia consisting of information from the WWW to scientific imaging to personal archives. Comprehensive review and survey papers that offer up new insights, and lay the foundations for further exploratory and experimental work, are also relevant. Relevant topics include Image and video retrieval - theory, algorithms, and systems Social media interaction and retrieval - collaborative filtering, social voting and ranking Music and audio retrieval - theory, algorithms, and systems Scientific and Bio-imaging - MRI, X-ray, ultrasound imaging analysis and retrieval Semantic learning - visual concept detection, object recognition, and tag learning Exploration of media archives - browsing, experiential computing Interfaces - multimedia exploration, visualization, query and retrieval Multimedia mining - life logs, WWW media mining, pervasive media analysis Interactive search - interactive learning and relevance feedback in multimedia retrieval Distributed and high performance media search - efficient and very large scale search Applications - preserving cultural heritage, 3D graphics models, etc. Editorial Policies: We aim for a fast decision time (less than 4 months for the initial decision) There are no page charges in IJMIR. Papers are published on line in advance of print publication. Academic, industrial researchers, and practitioners involved with multimedia search, exploration, and mining will find IJMIR to be an essential source for important results in the field.
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