Imbalanced Classification in Medical Imaging via Regrouping
This addresses imbalanced classification problems in medical imaging, offering a novel alternative to existing methods.
The paper tackles imbalanced classification in medical imaging by regrouping majority classes into smaller classes to create balanced multiclass classification, which substantially boosts performance with approximate AUPRC improvements.
We propose performing imbalanced classification by regrouping majority classes into small classes so that we turn the problem into balanced multiclass classification. This new idea is dramatically different from popular loss reweighting and class resampling methods. Our preliminary result on imbalanced medical image classification shows that this natural idea can substantially boost the classification performance as measured by average precision (approximately area-under-the-precision-recall-curve, or AUPRC), which is more appropriate for evaluating imbalanced classification than other metrics such as balanced accuracy.