动量对比的Voxel智能表示学习用于半监督的体积医疗图像细分
Chenyu You1, Ruihan Zhao2, Lawrence Staib1,3,4
1Electrical Engineering, Yale University, New Haven, CT USA.
Medical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention
|July 19, 2023
概括
用于医疗图像分割的对比学习 (CL) 改善了没有注释的特征表示. 我们的新的对比的Voxel智能表示学习 (CVRL) 方法增强了特征多样性,并捕捉了3D空间背景,以获得卓越的细分性能.
科学领域:
- 医学图像分析 医学图像分析
- 计算机视觉 计算机视觉
- 机器学习 机器学习
背景情况:
- 对比式学习 (CL) 用于医疗图像细分,以学习没有专家注释的表示.
- 当前的CL方法通常将所有输入特征映射到单个向量中,这可能导致特征崩并忽视图像中的解剖变化.
研究的目的:
- 引入一种新的对比的Voxel智能表示学习 (CVRL) 方法,以改进医疗图像细分.
- 为了解决现有的CL方法的局限性,特别是特征崩和在图像中假设均的阳性.
主要方法:
- 开发了一个新的CL策略,以促进跨3D表示维度的特征多样性.
- 实施了两级对比优化框架 (低级和高级) 用于培训3D医学图像.
- 沿着特征和批量尺寸捕获3D空间背景和解剖信息.
主要成果:
- 证明了CVRL框架在两个基准数据集上的优越性,跨越各种标签设置.
- 展示了该方法有效学习低级和高级特征的能力.
- 证实,拟议的方法继承了标准CL方法的硬度感知属性.
结论:
- 通过提高特征多样性和利用3D空间环境,CVRL方法有效地学习用于医疗图像细分的强大表示.
- 双层对比优化策略在克服现有CL技术的局限性方面被证明是有效的.
- 拟议的方法为无监督和半监督的医疗图像细分提供了一个有希望的方向.
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