CCA:用于监督和半监督医疗图像细分的对比集群分配.
Jinghua Zhu1, Chengying Huang1, Heran Xi2
1School of Computer Science and Technology, Heilongjiang University, Harbin, 150000, China.
概括
集群学习和对比集群分配 (CCA) 通过增强像素和类特征之间的交叉注意力来改善基于变压器的医疗图像细分. 这种插件方法提高了监督和半监督细分任务的性能.
科学领域:
- 计算机视觉 计算机视觉
- 医学图像分析 医学图像分析
- 人工智能的人工智能
背景情况:
- 变压器模型在语义细分方面表现有希望,但往往忽视了像素和类特征之间的关键交叉注意力.
- 这种限制阻碍了变压器在复杂的视觉任务中充分发挥其潜力,特别是在医学成像中.
研究的目的:
- 引入一种新的方法,集群学习和对比集群分配 (CCA),以增强基于变压器的医疗图像细分.
- 改进对象查询的集成,以更好地进行特征级别的聚类和像素类预测.
主要方法:
- 开发了集群学习,以将对象查询与功能级集群中心对齐.
- 引入了对比的集群分配 (CCA) 来指导使用学习集群中心的像素类预测.
- 为监督 (解码器增强) 和半监督 (编码器增强) 分段网络设计的插件模块.
主要成果:
- 在多个公共医疗图像数据集 (ACDC,LA,Synapse,ISIC2018) 中,与最先进的模型相比,表现一致.
- 验证了CCA在改善像素级预测和特征提取能力方面的有效性.
- 展示了CCA的多功能性,作为适用于各种基于变压器的架构的插件模块.
结论:
- 拟议的集群学习和对比集群赋值 (CCA) 方法显著推进了使用变压器的医疗图像细分.
- CCA有效地解决了交叉注意力的局限性,从而在监督和半监督环境中实现了卓越的性能.
- CCA的插件性质允许广泛应用和增强现有的细分模型.
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