Anti-Interference From Noisy Labels: Mean-Teacher-Assisted Confident Learning for Medical Image Segmentation
Zhe Xu, Donghuan Lu, Jie Luo, Yixin Wang, Jiangpeng Yan, Kai Ma, Yefeng Zheng, Raymond Kai‐Yu Tong
Abstract
Manually segmenting medical images is expertise-demanding, time-consuming and laborious. Acquiring massive high-quality labeled data from experts is often infeasible. Unfortunately, without sufficient high-quality pixel-level labels, the usual data-driven learning-based segmentation methods often struggle with deficient training. As a result, we are often forced to collect additional labeled data from multiple sources with varying label qualities. However, directly introducing additional data with low-quality noisy labels may mislead the network training and undesirably offset the efficacy provided by those high-quality labels. To address this issue, we propose a Mean-Teacher-assisted Confident Learning (MTCL) framework constructed by a teacher-student architecture and a label self-denoising process to robustly learn segmentation from a small set of high-quality labeled data and plentiful low-quality noisy labeled data. Particularly, such a synergistic framework is capable of simultaneously and robustly exploiting (i) the additional dark knowledge inside the images of low-quality labeled set via perturbation-based unsupervised consistency, and (ii) the productive information of their low-quality noisy labels via explicit label refinement. Comprehensive experiments on left atrium segmentation with simulated noisy labels and hepatic and retinal vessel segmentation with real-world noisy labels demonstrate the superior segmentation performance of our approach as well as its effectiveness on label denoising.