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视觉变压器蒸,用于在无线囊内镜图像中增强胃肠道异常识别

Yassine Oukdach¹, Anass Garbaz¹, Zakaria Kerkaou¹

¹Ibn Zohr University, LabSIV, Department of Computer Science, Faculty of Sciences, Agadir, Morocco.

Journal of medical imaging (Bellingham, Wash.)

|February 7, 2025

概括

这项研究引入了一个深度学习模型,使用知识蒸来改善无线囊内镜图像中的胃肠道异常检测. 人工智能系统准确地识别问题,帮助医生更快地进行诊断.

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) 产生了大量的图像,使胃肠道异常的手动检查耗时且昂贵.
计算机视觉辅助系统对于提高WCE图像分析的效率和准确性至关重要.

研究的目的:

开发一种深度学习方法,用于识别WCE图像中的胃肠道异常.
利用知识蒸 (KD) 来将CNN教师的学习转移到ViT学生模型.

主要方法:

使用了具有注意力机制和深度可分离的卷积的卷积神经网络 (CNN) 教师模型.
一个视力转换器 (ViT) 学生模型通过KD从CNN老师那里学习了代表.
这些模型在公开的WCE图像数据集 (Kvasir和KID) 上进行了训练和评估.

主要成果:

拟议的KD方法实现了高准确性:Kvasir的97%和KID数据集的96%.
该系统有效地区分正常和异常区域,以及出血与非出血病例.
该方法展示了计算效率和概括能力,超过了现有的最先进的技术.

结论:

使用CNN,ViT和KD的深度学习方法在WCE图像中有效地分类胃肠道疾病.
该系统显示出有希望的性能,为肠道疾病应用提供计算效率.
这种由人工智能驱动的工具可以显著帮助诊断由WCE引起的胃肠道疾病.

关键词:

注意力机制注意力机制卷积神经网络是一种卷积神经网络.检测胃肠道异常的检测知识的蒸知识的蒸.视觉变压器视觉变压器无线囊内镜无线囊内镜

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