Semi-Supervised Medical Image Classification Combined with Unsupervised Deep Clustering

An effective way to improve the performance of deep neural networks in most computer vision tasks is to improve the quantity of labeled data and the quality of labels. However, in the analysis and processing of medical images, high-quality annotation depends on the experience and professional knowledge of experts, which makes it very difficult to obtain a large number of high-quality annotations. Therefore, we propose a new semi-supervised framework for medical image classification. It combines semi-supervised classification with unsupervised deep clustering. Spreading label information to unlabeled data by alternately running two tasks helps the model to extract semantic information from unlabeled data, and prevents the model from overfitting to a small amount of labeled data. Compared with current methods, our framework enhances the robustness of the model and reduces the influence of outliers. We conducted a comparative experiment on the public benchmark medical image dataset to verify our method. On the ISIC 2018 Dataset, our method surpasses other methods by more than 0.85% on AUC and 1.08% on Sensitivity. On the ICIAR BACH 2018 dataset, our method achieved 94.12% AUC, 77.92% F1-score, 77.69% Recall, and 78.16% Precision. The error rate is at least 1.76% lower than that of other methods. The result shows the effectiveness of our method in medical image classification.