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在无线囊内镜中深度学习性能的审查图像用于胃肠道疾病分类

Tsedeke Temesgen Habe¹, Keijo Haataja¹, Pekka Toivanen¹

¹School of Computing, Faculty of Science, Forestry and Technology, University of Eastern Finland, Joensuu, North Karelia, 70211, Finland.

|October 28, 2024

概括

深度学习显著增强无线囊内镜 (WCE) 图像分析用于消化系统疾病诊断. 本综述涵盖了先进的AI技术,如转移学习和注意力机制,解决WCE的挑战.

科学领域:

医疗成像医学成像
人工智能的人工智能
胃肠病学胃肠病学

背景情况:

无线囊内镜 (WCE) 是检查消化道的一个关键的非侵入性工具.
由于低分辨率和文物,WCE图像分析很困难.
深度学习 (DL) 显示了改善WCE图像解释的潜力.

研究的目的:

审查WCE深度学习的当前趋势和未来方向.
为了突出最近的DL技术的进步适用于WCE.
识别 WCE 的 DL 的挑战和机遇.

主要方法:

关于WCE中的深度学习应用的最新文献的审查.
专注于特定的DL技术:转移学习,注意力机制,多模式学习,自动化损伤检测,可解释性,数据增强和边缘计算.
讨论挑战,局限性和未来的研究途径.

主要成果:

深度学习方法正在推进WCE图像分析,提高诊断准确度.
转移学习和注意力机制等技术在克服WCE图像质量问题方面表现有前途.
自动化病变检测和增强的解释性是开发的关键领域.

结论:

深度学习有很大的潜力,可以彻底改变无线囊内镜分析.
对WCE的DL的持续研究对于推进消化系统疾病诊断和监测至关重要.
本综述是该领域的研究人员和临床医生的宝贵参考资料.

关键词:

注意力机制注意力机制自动化损伤检测检测器数据增强数据增强深度学习是一种深度学习.边缘计算.边缘计算.可解释性和可解释性多模式学习是多模式学习.转移学习转移学习无线囊内镜无线囊内镜

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