  Supervised deep learning performance is heavily tied to the availability of
high-quality labels for training. Neural networks can gradually overfit
corrupted labels if directly trained on noisy datasets, leading to severe
performance degradation at test time. In this paper, we propose a novel deep
learning framework, namely Co-Seg, to collaboratively train segmentation
networks on datasets which include low-quality noisy labels. Our approach first
trains two networks simultaneously to sift through all samples and obtain a
subset with reliable labels. Then, an efficient yet easily-implemented label
correction strategy is applied to enrich the reliable subset. Finally, using
the updated dataset, we retrain the segmentation network to finalize its
parameters. Experiments in two noisy labels scenarios demonstrate that our
proposed model can achieve results comparable to those obtained from supervised
learning trained on the noise-free labels. In addition, our framework can be
easily implemented in any segmentation algorithm to increase its robustness to
noisy labels.
