  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.
