Beyond nearest neighbor interpolation in data augmentation

Abstract 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.