Active Label Refinement for Robust Training of Imbalanced Medical Image Classification Tasks in the Presence of High Label Noise
Published in International Conference on Medical Image Computing and Computer-Assisted Intervention, 2024
Abstract: The robustness of supervised deep learning-based medical image classification is significantly undermined by label noise in the training data. 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 incor- porating experts-in-the-loop for actively cleaning the noisy labels. To mitigate these challenges, we propose a two-phase approach that com- bines Learning with Noisy Labels (LNL) and active learning. This ap- proach not only improves the robustness of medical image classification in the presence of noisy labels but also iteratively improves the qual- ity of the dataset by relabeling the important incorrect labels, under a limited annotation budget. Furthermore, we introduce a novel Variance of Gradients approach in the LNL phase, which complements the loss- based sample selection by also sampling under-represented examples. Us- ing two imbalanced noisy medical classification datasets, we demonstrate that our proposed technique is superior to its predecessors at handling classimbalancebynotmisidentifyingcleansamplesfromminorityclasses as mostly noisy samples. Code available at: Bidur- Khanal/imbalanced-medical-active-label-cleaning.git
Bidur Khanal, Tianhong Dai, Binod Bhattarai, and Cristian A. Linte. “Active Label Refinement for Robust Training of Imbalanced Medical Image Classification Tasks in the Presence of High Label Noise.” In International Conference on Medical Image Computing and Computer-Assisted Intervention. Cham: Springer Nature Switzerland, 2024.