Accurate Segmentation of Optic Disc And Cup from Multiple Pseudo-labels by Noise-aware Learning

arXiv:2311.18496v2 Announce Type: replace
Abstract: Optic disc and cup segmentation plays 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 objects 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 …

arxiv cs.cv labels multiple noise optic segmentation type