  The classification of glomerular lesions is a routine and essential task in
renal pathology. Recently, machine learning approaches, especially deep
learning algorithms, have been used to perform computer-aided lesion
characterization of glomeruli. However, one major challenge of developing such
methods is the naturally imbalanced distribution of different lesions. In this
paper, we propose CircleMix, a novel data augmentation technique, to improve
the accuracy of classifying globally sclerotic glomeruli with a hierarchical
learning strategy. Different from the recently proposed CutMix method, the
CircleMix augmentation is optimized for the ball-shaped biomedical objects,
such as glomeruli. 6,861 glomeruli with five classes (normal, periglomerular
fibrosis, obsolescent glomerulosclerosis, solidified glomerulosclerosis, and
disappearing glomerulosclerosis) were employed to develop and evaluate the
proposed methods. From five-fold cross-validation, the proposed CircleMix
augmentation achieved superior performance (Balanced Accuracy=73.0%) compared
with the EfficientNet-B0 baseline (Balanced Accuracy=69.4%)
