Sample Selection Based on Uncertainty for Combating Label Noise

Automatic segmentation of medical images plays a crucial role in scientific research and healthcare. Obtaining large-scale training datasets with high-quality manual annotations poses challenges in many clinical applications. Utilizing noisy datasets has become increasingly important, but label noise significantly affects the performance of deep learning models. Sample selection is an effective method for handling label noise. In this study, we propose a medical image segmentation framework based on entropy estimation uncertainty for sample selection to address datasets with noisy labels. Specifically, after sample selection, parallel training of two networks and cross-model information exchange are employed for collaborative optimization learning. Based on the exchanged information, sample selection is performed using entropy estimation uncertainty, following a carefully designed schedule for gradual label filtering and correction of noisy labels. The framework is flexible in terms of the precise deep neural network (DNN) models used. Method analysis and empirical evaluation demonstrate that our approach exhibits superior performance on open datasets with noisy annotations. The sample selection method outperforms small loss criterion approaches, and the segmentation results surpass those of traditional fully supervised models. Our framework provides a valuable solution for effectively handling noisy label datasets in medical image segmentation tasks.