Attention Shift: Interpretability Study of Texture-based Data Augmentation in Training U-Net Models for Brain Image Segmentation

Texture smoothing has recently become a promising data augmentation method to enhance the performance of deep learning segmentation methods in medical image analysis. However, a deeper understanding of this phenomenon has not been investigated. In this study, we investigated this phenomenon using a controlled experimental setting, using datasets from the Human Connectome Project, in order to mitigate the inhomogeneity of data confounders to the network, and investigate possible explanations as to why model performance changes when applying different levels of total variation smoothing during data augmentation. Through experiments we confirm previous findings regarding the benefits of smoothing during data augmentation, but further report that the regime of improvement is limited and it changes in relation to the selected imaging protocol. We also found that smoothing during data augmentation produces a spatial attention shift also associated with different performance levels of the trained segmentation model.