论文标题
通过重组的医学成像中的分类不平衡
Imbalanced Classification in Medical Imaging via Regrouping
论文作者
论文摘要
我们建议通过将多数类重新组合为小类来执行不平衡的分类，以便将问题变成平衡的多类分类。这个新想法与流行的损失重新加权和班级重采样方法截然不同。我们对不平衡的医学图像分类的初步结果表明，这种自然想法可以大大提高按平均精度（大约不超过precision-reciss-recall-curve或auprc）来衡量的分类性能，这比其他度量标准（如平衡准确性）更适合评估不平衡分类。
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.
