A Deep Convolutional Neural Network-Based Label Completion and Correction Strategy for Supervised Medical Image Learning

In supervised medical image learning, labels for disease images are indispensable. The correctness of labels has a significant influence on disease discrimination. However, the labelling of disease images by professional doctors is a time-consuming and labour-intensive project; therefore, obtaining correct and high-quality labels is a difficult task in medical image learning. This paper proposes a deep convolutional neural network-based label completion and correction strategy for supervised medical image learning. We take the diagnosis of Seborrheic Keratosis (SK) and Flat Wart (FW) as examples and perform the label completion and correction on a dataset containing confocal laser scanning microscope images. Experimental results show that this strategy can use limited labelled data to complete the labels for most of the unlabelled data and correct some noise labels in the dataset, which improves the accurate rate of the model to identify diseases.