Noise‐robust neural networks for medical image segmentation by dual‐strategy sample selection

Summary Deep neural networks for medical image segmentation often face the problem of insufficient clean labeled data. Although non‐expert annotations are more readily accessible, these low‐quality annotations lead to significant performance degradation of existing neural network methods. In this paper, we focus on robust learning of medical image segmentation with noisy annotations and propose a novel noise‐tolerant framework based on dual‐strategy sample selection, which selects the informative samples to provide effective supervision information. First, we propose the first round of sample selection by designing a novel joint loss, which includes conventional supervised loss and regularization loss. To further select information‐rich samples, we propose confidence‐based pseudo‐label sample selection from a novel perspective as the complement. The dual strategies are used in a collaborative manner and the network is optimized with mined informative samples. We conducted extensive experiments on datasets with both simulated noisy labels and real‐world noisy labels. For instance, on a simulated dataset with 25% noise ratio, our method achieves segmentation Dice value with 90.56% 0.03%. Furthermore, increasing the noise ratio to 95%, our method still maintains a high Dice value of 73.85% 0.28% compared to other baselines. Extensive results have demonstrated that our method can weaken the effects of noisy labels on medical image segmentation.