用多式模式,超级模式和基于变压器的学习来对性结肠炎严重程度进行分类的三重方法.
Md Nasim Ahmed1, Dipta Neogi1, Muhammad Rafsan Kabir2
1Department of Electrical and Computer Engineering, North South University, Dhaka, 1229, Bangladesh.
Scientific reports
|July 27, 2025
概括
深度学习模型准确地使用视觉转换器和多模式方法对性结肠炎 (UC) 严重程度进行分类. 可解释的人工智能增强了对这些先进诊断工具的临床信心.
科学领域:
- 人工智能的人工智能
- 医疗成像医学成像
- 胃肠病学 胃肠病学
背景情况:
- 性结肠炎 (UC) 需要精确的严重程度分类才能得到有效的治疗.
- 目前的方法可能缺乏最佳治疗决策所需的准确性.
研究的目的:
- 开发和评估深度学习模型来对性结肠炎的严重程度进行分类.
- 探索视觉转换器 (ViT),多式学习和少数镜头元学习的有效性.
主要方法:
- 采用了三重的深度学习方法:多式推理,少数拍摄的元学习和基于ViT的组合.
- 评估了各种ViT架构 (Swin-Base) 和组合,以及多式联网预训练框架 (CLIP,BLIP,FLAVA) 和少数拍摄方法 (匹配网络).
- 为了模型的可解释性,使用了夏普利添加式解释 (SHAP).
主要成果:
- 一个Swin-Base ViT模型达到90%的准确性;一个多样化的ViT组合达到93%的准确性.
- 采用常规ML的多式联络框架获得了83%的准确性.
- 几次射击的元学习在5次射击的设置中达到83%的准确性.
结论:
- 深度学习,特别是ViT组合和多式联络策略,显示了准确的UC严重程度分类的巨大潜力.
- 通过SHAP的模型解释性对于建立对人工智能驱动的医学图像分析的临床信任至关重要.
- 这些先进的技术提供了强大的解决方案,以提高UC管理中的诊断精度.
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