从结肠镜图像进行炎症性肠病分类的子类意识对比性半监督学习
Kechen Lin1,2, Guangcong Ruan1, Xiaoyang Zou2
1Department of Gastroenterology, Daping Hospital, Army Medical University (Third Military Medical University), Chongqing 400042, China.
Bioengineering (Basel, Switzerland)
|January 28, 2026
概括
这项研究引入了一种新的半监督学习方法,SACSSL,用于从结肠镜图像中分类炎症性肠病 (IBD). 通过解决不准确的伪标签,SACSSL提高了准确性,通过有限的标签数据实现近乎最先进的结果.
科学领域:
- 医疗成像医学成像
- 计算机视觉 计算机视觉
- 机器学习 机器学习
背景情况:
- 从结肠镜图像准确分类炎症性肠病 (IBD),包括克罗恩病 (CD) 和性结肠炎 (UC),对于患者的诊断和治疗至关重要.
- 对于IBD分类的深度学习模型需要大量的标记数据,这往往很少,这构成了重大挑战.
- 使用伪标签的现有半监督学习方法与IBD图像进行斗争,原因是高的类内变化和微妙的类间差异,导致不准确的伪标签和确认偏见.
研究的目的:
- 开发一种先进的半监督学习方法,分类意识的对比半监督学习 (SACSSL),以从结肠镜图像中准确地分类IBD.
- 通过减轻确认偏差和处理类内异质性来克服传统伪标签方法的局限性.
- 通过使用有限的标记数据来提高IBD分类的深度学习模型的性能.
主要方法:
- 拟议的SACSSL方法将一个分类意识的对比模块集成到一个伪标签框架中 (例如,FixMatch).
- 不确定的未标记样品被处理为实例级对比损失,以减少确认偏差.
- 自信的未标记样本被用原型和监督的对比损失集成到细粒度子类中,以改善类间可分离性和类内多样性.
主要成果:
- 在半监督的环境中,SACSSL在内部IBD分类数据集 (Daping) 和公共UC严重程度分级数据集 (LIMUC) 上实现了最先进的性能.
- 只有20%的标记数据,SACSSL在Dapping数据集上达到93.2%的准确性和80.1%的F1分数,接近完全监督的性能.
- 在LIMUC数据集上,SACSSL获得了76.4%的准确率和68.9%的F1分数,证明了它在不同IBD相关任务中的有效性.
结论:
- 拟议的SACSSL方法有效地提高了半监督结肠镜图像对IBD的分类.
- 在IBD图像分析中,SACSSL成功地解决了不准确的伪标签,确认偏差和类内变异性的挑战.
- 该方法显示了在有限的标记数据下改善IBD诊断和治疗分层的巨大潜力.
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