Automatic Caption Generation for Medical Images

I. Allaouzi, M. Ben ahmed, B. Benamrou, Mustapha Ouardouz
{"title":"Automatic Caption Generation for Medical Images","authors":"I. Allaouzi, M. Ben ahmed, B. Benamrou, Mustapha Ouardouz","doi":"10.1145/3286606.3286863","DOIUrl":null,"url":null,"abstract":"With the increasing availability of medical images coming from different modalities (X-Ray, CT, PET, MRI, ultrasound, etc.), and the huge advances in the development of incredibly fast, accurate and enhanced computing power with the current graphics processing units. The task of automatic caption generation from medical images became a new way to improve healthcare and the key method for getting better results at lower costs. In this paper, we give a comprehensive overview of the task of image captioning in the medical domain, covering: existing models, the benchmark medical image-caption datasets, and evaluation metrics that have been used to measure the quality of the generated captions.","PeriodicalId":416459,"journal":{"name":"Proceedings of the 3rd International Conference on Smart City Applications","volume":"1 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2018-10-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"17","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Proceedings of the 3rd International Conference on Smart City Applications","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1145/3286606.3286863","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 17

Abstract

With the increasing availability of medical images coming from different modalities (X-Ray, CT, PET, MRI, ultrasound, etc.), and the huge advances in the development of incredibly fast, accurate and enhanced computing power with the current graphics processing units. The task of automatic caption generation from medical images became a new way to improve healthcare and the key method for getting better results at lower costs. In this paper, we give a comprehensive overview of the task of image captioning in the medical domain, covering: existing models, the benchmark medical image-caption datasets, and evaluation metrics that have been used to measure the quality of the generated captions.
医学图像的自动标题生成
随着来自不同模式(x射线、CT、PET、MRI、超声波等)的医学图像的日益可用性,以及当前图形处理单元在令人难以置信的快速、准确和增强的计算能力方面的巨大进步。从医学图像中自动生成标题任务成为改善医疗保健的新途径,也是以更低的成本获得更好结果的关键方法。在本文中,我们对医学领域的图像字幕任务进行了全面的概述,包括:现有模型,基准医学图像字幕数据集,以及用于衡量生成的字幕质量的评估指标。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
求助全文
约1分钟内获得全文 求助全文
来源期刊
自引率
0.00%
发文量
0
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
确定
请完成安全验证×
copy
已复制链接
快去分享给好友吧!
我知道了
右上角分享
点击右上角分享
0
联系我们:info@booksci.cn Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。 Copyright © 2023 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术官方微信