改进医学图像分割的数据增强，避免插值导致的标签错误和细节丢失。
Beyond Nearest Neighbor Interpolation in Data Augmentation
- 用几何变换替代最近邻插值，减少像素级标注误差。
- 引入均值分类过滤机制，解决插值后类别标签不确定问题。
- 在3个医学影像数据集上验证，显著提升分割性能。
使用最近邻插值进行数据增强时，虽避免了未定义类别标签的风险，却可能加剧训练数据中的像素级标注误差。此外，插值算法固有的低通滤波效应会削弱标注区域内的高频结构细节。为规避这些风险，作者修改了卷积神经网络的数据变换函数，采用改进的几何变换函数，不再依赖最近邻插值，并集成基于均值的类别过滤机制，以处理其他插值算法带来的未定义类别标签问题。同时，构建了离线数据增强流程，生成特定于插值方式的增强训练数据，实现对插值导致的低通滤波效应的定量评估。在三个医学图像分割数据集及XBAT+数据集上的实验表明，该方法在多个定量指标上均取得性能提升。
原文摘要 · Abstract (English)
Avoiding the risk of undefined categorical labels using nearest neighbor interpolation overlooks the risk of exacerbating pixel level annotation errors in augmented training data. Additionally, the inherent low pass filtering effects of interpolation algorithms exacerbate the risk of degrading high frequency structural details within annotated regions of interest. To avoid these risks, the author modified convolutional neural networks data transformation functions by incorporating a modified geometric transformation function, removing reliance on nearest neighbor interpolation, and integrating a mean-based class filtering mechanism to handle undefined categorical labels with alternative interpolation algorithms. The author also implemented an offline data augmentation pipeline to generate interpolation specific augmented training data, enabling quantitative assessment of interpolation specific low pass filtering effects on augmented training data. Experimental evaluation on three medical image segmentation datasets and the XBAT+ datasets demonstrated performance gains across multiple quantitative metrics.
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