The robustness of supervised deep learning-based medical image classification is significantly undermined by label noise. Although several methods have been proposed to enhance classification performance in the presence of noisy labels, they face some challenges: 1) a struggle with class-imbalanced datasets, leading to the frequent overlooking of minority classes as noisy samples; 2) a singular focus on maximizing performance using noisy datasets, without incorporating experts-in-the-loop for actively cleaning the noisy labels. To mitigate these challenges, we propose a two-phase approach that combines Learning with Noisy Labels (LNL) and active learning. This approach not only improves the robustness of medical image classification in the presence of noisy labels, but also iteratively improves the quality of the dataset by relabeling the important incorrect labels, under a limited annotation budget. Furthermore, we introduce a novel Variance of Gradients approach in LNL phase, which complements the loss-based sample selection by also sampling under-represented samples. Using two imbalanced noisy medical classification datasets, we demonstrate that that our proposed technique is superior to its predecessors at handling class imbalance by not misidentifying clean samples from minority classes as mostly noisy samples.

这篇研究提出了一种两阶段的方法，结合学习与不确定标签以及主动学习，来提高医学图像分类在存在噪声标签的情况下的鲁棒性，并通过有限的注释预算迭代地改进数据集的质量。同时，引入了新颖的梯度方差方法，辅助基于损失函数的样本选择，同时采样了少数类别样本，以处理类别不平衡问题。实验证明，该方法优于之前的方法，避免将较干净的少数类别样本错误地分类为主要是噪声的样本。

存在高标签噪声的不平衡医学图像分类任务的主动标签精炼强化训练