Semi-Supervised Medical Image Classification Based on Sample Intrinsic Similarity Using Canonical Correlation Analysis

Large amounts of labeled data are usually needed for training deep neural networks in medical image studies, particularly in medical image classification. However, in the field of semi-supervised medical image analysis, labeled data is very scarce due to patient privacy concerns. For researchers, obtaining high-quality labeled images is exceedingly challenging because it involves manual annotation and clinical understanding. In addition, skin datasets are highly suitable for medical image classification studies due to the inter-class relationships and the inter-class similarities of skin lesions. In this paper, we propose a model called Coalition Sample Relation Consistency (CSRC), a consistency-based method that leverages Canonical Correlation Analysis (CCA) to capture the intrinsic relationships between samples. Considering that traditional consistency-based models only focus on the consistency of prediction, we additionally explore the similarity between features by using CCA. We enforce feature relation consistency based on traditional models, encouraging the model to learn more meaningful information from unlabeled data. Finally, considering that cross-entropy loss is not as suitable as the supervised loss when studying with imbalanced datasets (i.e., ISIC 2017 and ISIC 2018), we improve the supervised loss to achieve better classification accuracy. Our study shows that this model performs better than many semi-supervised methods.