Abstract
The presence of corrupted labels is a common problem in the medical image datasets due to the difficulty of annotation. Meanwhile, corrupted labels might significantly deteriorate the performance of deep neural networks (DNNs), which have been widely applied to medical image analysis. To alleviate this issue, in this paper, we propose a novel framework, namely Co-assistant Networks for Label Correction (CNLC), to simultaneously detect and correct corrupted labels. Specifically, the proposed framework consists of two modules, i.e., noise detector and noise cleaner. The noise detector designs a CNN-based model to distinguish corrupted labels from all samples, while the noise cleaner investigates class-based GCNs to correct the detected corrupted labels. Moreover, we design a new bi-level optimization algorithm to optimize our proposed objective function. Extensive experiments on three popular medical image datasets demonstrate the superior performance of our framework over recent state-of-the-art methods. Source codes of the proposed method are available on https://github.com/shannak-chen/CNLC.
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Acknowledgements
This paper is supported by NSFC 62276052, Medico-Engineering Cooperation Funds from University of Electronic Science and Technology of China (No. ZYGX2022YGRH009 and No. ZYGX2022YGRH014).
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Chen, X., Fu, W., Li, T., Shi, X., Shen, H., Zhu, X. (2023). Co-assistant Networks for Label Correction. In: Greenspan, H., et al. Medical Image Computing and Computer Assisted Intervention – MICCAI 2023. MICCAI 2023. Lecture Notes in Computer Science, vol 14222. Springer, Cham. https://doi.org/10.1007/978-3-031-43898-1_16
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