Study on TNM Classification Diagnosis of Colorectal Cancer Based on Improved Self-supervised Contrast Learning

TNM classification of colorectal cancer is of great significance for doctors to make clinical decision, evaluate patient prognosis and improve treatment. The diagnosis results of TNM classification of colorectal cancer combined with multi-modal medical data are often more accurate than those based on single modal medical data. However, how to balance the redundancy and complementarity of multimodal medical data in deep learning is a difficult problem. Considering the expensive collection and labeling of medical data, we propose an improved self-supervised contrastive learning method for TNM classification of colorectal cancer. Self-supervised contrast learning guides the feature representation of the learning data according to its own supervised information. In the feature extraction stage, we used a deep convolutional neural network with a spatial-channel attention module to extract the features of MRI images, and incorporated clinicopathological parameters in the fully connected layer to achieve multi-modal data fusion. In the comparison of feature similarity, Mahalanobis distance measurement learning method is used to eliminate the scale interference of features between different models. In the contrast loss stage, we design a contrast loss function suitable for multimodal feature representation for model training. In order to verify the effectiveness of the proposed method, experiments were carried out on the collected data sets. The experimental results show that compared with traditional methods, the TNM staging diagnosis technique proposed in this study based on improved self-supervised comparative learning has achieved significant improvement in accuracy and recall rate.