Contrastive Learning Joint Regularization for Pathological Image Classification with Noisy Labels
Wenping Guo,
Gang Han,
Yaling Mo
et al.
Abstract:The annotation of pathological images often introduces label noise, which can lead to overfitting and notably degrade performance. Recent studies have attempted to address this by filtering samples based on the memorization effects of DNNs. However, these methods often require prior knowledge of the noise rate or a small, clean validation subset, which is extremely difficult to obtain in real medical diagnosis processes. To reduce the effect of noisy labels, we propose a novel training strategy that enhances n… Show more
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