Robustness evaluation against corruptions for Optical Diffraction Tomography-based classifiers

Abstract

Optical Diffraction Tomography (ODT) is a promising technique for three-dimensional imaging, but practical use demands rigorous robustness testing due to real-world noise factors. Despite the growing importance of machine learning safety, robustness in ODT remains underexplored. We propose the first comprehensive robustness testing protocol for ODT-based classifiers by simulating 16 corruption scenarios to create a corrupted dataset. To enhance robustness and accuracy, we introduce CutPix, a data augmentation strategy that balances shape and texture information through fractal pattern mixing and a cut-and-concatenate approach. Our experiments show that CutPix significantly improves robustness under various corrupted environments compared to existing techniques, particularly against pattern noises. All code and corruption simulation scripts are publicly available at https://github.com/NySunShine/odt-robustness-evaluation