DIER-Net: Debiased Learning With Medical Image Noisy Label by Intrinsic and Extrinsic Regularization

Abstract

In medical image analysis, the presence of noisy labels and imbalanced data poses significant challenges to the performance of deep learning models, particularly in critical diagnostic tasks. To address this issue, we propose DIER-Net, a learning with noisy label framework designed to handle noisy labels in imbalanced medical datasets. Our approach introduces a debiased sample selection technique that effectively filters out noisy labels while preserving important minority class samples. Additionally, we employ intrinsic and extrinsic regularization strategies to enhance the model's robustness by leveraging both clean and noisy data. Our method is evaluated on two widely used medical image datasets: the ISIC melanoma classification and Kaggle histopathologic lymph node classification. The experimental results demonstrate that DIER-Net consistently outperforms existing state-of-the-art methods, particularly in settings with high levels of label noise, offering a robust solution for real-world clinical applications where noisy and imbalanced data are common. DIER-Net provides an effective approach to enhance the reliability of AI systems in medical imaging, contributing to more accurate and trustworthy diagnostic outcomes.