1 research outputs found
Imbalanced Classification in Medical Imaging via Regrouping
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