Improve Global Glomerulosclerosis Classification with Imbalanced Data using CircleMix Augmentation
This work addresses the challenge of imbalanced data in classifying glomerular lesions for renal pathology, representing an incremental improvement over existing methods.
The paper tackled the problem of classifying globally sclerotic glomeruli in renal pathology using deep learning, addressing data imbalance with a novel CircleMix augmentation technique, which achieved a balanced accuracy of 73.0% compared to a baseline of 69.4%.
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%)