用拼图拼图进行细粒度自主监督学习,用于医学图像分类
1Department of Software, Ajou University, Republic of Korea.
Computers in biology and medicine
|April 18, 2024
概括
很难对微妙的医疗病变进行分类. 细粒度自主监督学习 (FG-SSL) 方法使用等级块来改进医疗图像中的细粒度病变分类,优于现有方法.
科学领域:
- 医学成像分析 医学成像分析
- 计算机视觉 计算机视觉
- 机器学习 机器学习
背景情况:
- 在医学图像中对细粒度病变进行分类,由于微妙的视觉差异,因此存在挑战.
- 训练深层神经网络从这种微妙的差异中学习特征是很困难的.
研究的目的:
- 引入一种新的细粒度自主监督学习 (FG-SSL) 方法,以改进医疗图像中微妙损伤的分类.
- 增强医疗图像数据集中微妙差异的学习.
主要方法:
- FG-SSL方法采用一个层次的块结构来进行渐进式学习.
- 它强制执行细粒度拼图和规范原始图像之间的交叉相关性,以近似识别矩阵.
- 层次块也应用于监督学习,以加强微妙差异的发现.
主要成果:
- 拟议的FG-SSL方法与最先进的方法相比,表现良好.
- 对包括ISIC2018,APTOS2019和ISIC2017在内的综合医疗图像识别数据集进行了实验.
- 该方法不需要不对称的模型或负采样,并且对批量大小不敏感.
结论:
- FG-SSL方法有效地解决了医学成像中细粒度病变分类的挑战.
- 使用等级块的渐进式学习方法显著改善了细微差异的识别.
- 对于医疗图像分析任务,FG-SSL提供了强大而高效的解决方案.
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