级联大小依赖深度传播 (CADP):在图表中解决过度平滑问题 几次射击皮肤病学分类
Abdulrahman Noman1, Zou Beiji1, Chengzhang Zhu2
1School of Computer Science and Engineering, Central South University, Changsha, 410083, China.
概括
级联大小依赖深度传播 (CADP) 解决了图形神经网络中的过度平滑,以实现短暂的学习. 这种新的方法通过脱特征传播和优化标签传播来提高皮肤疾病分类的准确性.
科学领域:
- 机器学习 机器学习
- 计算机视觉 计算机视觉
- 医学图像分析 医学图像分析
背景情况:
- 图形神经网络 (GNN) 擅长于复杂的数据关系,但遭受过度平滑,降低节点表示.
- 过度平滑通过过度邻近聚合使节点特征均质化,限制了GNN的区分能力.
研究的目的:
- 引入级联大小依赖深度传播 (CADP),一种新的方法,以减轻基于图形的几次射击学习中的过度平滑.
- 通过改进特征表示和标签传播,通过使用CADP提高皮肤疾病分类准确性.
主要方法:
- 使用卷积神经网络 (CNN) 来构建图形,从图像中提取特征,以节点作为特征和边缘作为相似性.
- 分离特征从神经网络转换中传播,以实现更深层次的信息流并防止过度平滑.
- 通过多层感知器 (MLP) 将支持标签与查询图像预测集成,并通过由大小依赖的超参数 (K1,K2) 控制的深度标签传播进行优化.
主要成果:
- 在皮肤病数据集的双向5次射击设置中,CADP实现了高精度:78.3% (ISIC 2018),79.29% (Derm7pt) 和91.92% (SD-198).
- 拟议的方法在所有评估的数据集中,与现有方法相比,显示出更高的性能.
结论:
- CADP有效地减轻了用于医学图像分类的基于图形的几次拍摄学习中的过度平滑.
- 该方法显示了通过增强的基于图形的学习来提高皮肤病诊断准确性的重大前景.
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