Improve Global Glomerulosclerosis Classification with Imbalanced Data using CircleMix Augmentation

TL;DR

This paper introduces CircleMix, a novel data augmentation method tailored for biomedical spherical objects, which improves deep learning classification accuracy of glomerular lesions amidst data imbalance.

Contribution

The study presents CircleMix, a new augmentation technique optimized for ball-shaped objects, enhancing classification performance over existing methods like CutMix.

Findings

CircleMix outperforms baseline in balanced accuracy (73.0% vs. 69.4%)

Effective for classifying imbalanced glomerular lesion data

Applicable to biomedical spherical object classification

Abstract

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%)