An Automated Method of Identifying Incorrectly Labelled Images Based on the Sequences of Loss Functions of Deep Learning Networks

Zhipeng Zhang, Wenhui Shou, Wengting Ma, Dongjia Xing, Qingqing Xu, Li-Qun Xu, Qingxia Fan, Ling Xu

Abstract: Deep learning has been widely applied to medical image analysis tasks. Since the labelled medical images are the foundation of the training, validation, and test of deep learning classification models, the quality of labelling process could directly affect the performance of the models. However, it was estimated that up to ten percent of manually labelled medical images may be incorrectly labelled. In this paper, by utilizing the sequences of loss functions of deep learning classification networks through multiple training epochs, an automated method of identifying incorrectly labelled medical images was proposed. For those identified images, their labels could be further reviewed and updated by senior and experienced physicians, ultimately improving the quality of labelled medical image datasets, as well as the performance of the deep learning models.