Optic disc and cup segmentation play a crucial role in automating the
screening and diagnosis of optic glaucoma. While data-driven convolutional
neural networks (CNNs) show promise in this area, the inherent ambiguity of
segmenting object and background boundaries in the task of optic disc and cup
segmentation leads to noisy annotations that impact model performance. To
address this, we propose an innovative label-denoising method of Multiple
Pseudo-labels Noise-aware Network (MPNN) for accurate optic disc and cup
segmentation. Specifically, the Multiple Pseudo-labels Generation and Guided
Denoising (MPGGD) module generates pseudo-labels by multiple different
initialization networks trained on true labels, and the pixel-level consensus
information extracted from these pseudo-labels guides to differentiate clean
pixels from noisy pixels. The training framework of the MPNN is constructed by
a teacher-student architecture to learn segmentation from clean pixels and
noisy pixels. Particularly, such a framework adeptly leverages (i) reliable and
fundamental insights from clean pixels and (ii) the supplementary knowledge
within noisy pixels via multiple perturbation-based unsupervised consistency.
Compared to other label-denoising methods, comprehensive experimental results
on the RIGA dataset demonstrate our method's excellent performance and
significant denoising ability.

通过使用多伪标签噪声感知网络的创新标签去噪方法，本研究提出了准确识别光盘和杯子分割的方法，通过从伪标签中生成的像素级一致信息，提取干净像素和噪声像素之间的区别，有效改善了模型的性能。与其他标签去噪方法相比，在 RIGA 数据集上进行的全面实验结果显示本方法具有出色的性能和显著的去噪能力。