Mask-Aware Cropping: Mitigating Mask Imbalance in Segmentation Tasks

Data imbalance can take various forms, such as uneven class distributions in the dataset.Solutions like data augmentation, sampling techniques and weighted loss functions are commonly used to address this issue.However, in segmentation tasks, an additional type of imbalance may occur at the pixel-level, with most of them belonging to the background class.This work introduces Mask-Aware Cropping (MAC), a technique to reduce pixel-level imbalance by cropping image regions containing key information about the minority class.