Semi Supervised Medical Image Classification

Semi-supervised Medical Image Classification is a technique in medical image classification that combines supervised and unsupervised learning methods. The goal of this task is to improve the accuracy and robustness of medical image classification by utilizing a small amount of labeled data and a large amount of unlabeled data, thereby playing a significant role in medical diagnosis, disease screening, and treatment planning. By reducing the reliance on expensive and time-consuming labeled data, this approach can effectively enhance model training efficiency and generalization ability.