论文标题
解决皮肤病学中现实世界中的不平衡问题
Addressing the Real-world Class Imbalance Problem in Dermatology
论文作者
论文摘要
阶级失衡是医学诊断中的一个常见问题，导致标准分类器偏向普通阶层，并且在罕见类别上的表现不佳。对于皮肤病学尤其如此，这是一种具有数千种皮肤疾病的特色菜，但其中许多在现实世界中的患病率较低。在最近的进步中，我们探索了很少的射击学习方法以及针对皮肤状况识别问题的常规类失衡技术，并提出了评估设置，以公平地评估这种方法的现实世界实用性。我们发现几个节目学习方法的性能并不能达到传统类不平衡技术的表现，但是使用新颖的合奏结合两种方法可以改善模型性能，尤其是对于稀有类别。我们得出的结论是，结合对于解决阶级不平衡问题可能是有用的，但是通过现实世界中的评估设置，可以进一步加速进度，以实现新方法。
Class imbalance is a common problem in medical diagnosis, causing a standard classifier to be biased towards the common classes and perform poorly on the rare classes. This is especially true for dermatology, a specialty with thousands of skin conditions but many of which have low prevalence in the real world. Motivated by recent advances, we explore few-shot learning methods as well as conventional class imbalance techniques for the skin condition recognition problem and propose an evaluation setup to fairly assess the real-world utility of such approaches. We find the performance of few-show learning methods does not reach that of conventional class imbalance techniques, but combining the two approaches using a novel ensemble improves model performance, especially for rare classes. We conclude that ensembling can be useful to address the class imbalance problem, yet progress can further be accelerated by real-world evaluation setups for benchmarking new methods.
