Hierarchical few-shot learning based on top-down correction mechanism with stop strategy

Few-shot learning has become an important branch of machine learning, which aims to give correct prediction information to unknown samples. Many few-shot models mostly adopt a hierarchical structure and make good use of the sample information at different levels of granularity to finish class prediction. However, existing few-shot learning only focuses on the amount of information correctly predicted and ignores the different risk levels of an incorrect prediction. For example, in the field of medical image pathology analysis, difficult pathological images can only be roughly distinguished as whether they are diseased and cannot be specifically analyzed for the degree of disease. In this paper, we propose a hierarchical few-shot learning based on top-down correction mechanism with stop strategy (HTDS) for few-shot classification. Firstly, we get the coarse-grained of each class by taking the mean value for each class, and we use the coarse-grained and the original fine-grained samples to build a decision tree with a multi-granularity hierarchical structure. The decision tree represents the class relationship among samples. Secondly, a threshold is established to measure the impact of coarse-grained on test sample classification. It stops the correct classification at the coarse-grained level and transfers the incorrect classification down to the fine-grained level. Finally, the fine-grained layer assists the coarse-grained layer in classification via a top-down correction mechanism. It minimizes the coarse-grained risk of misclassification. By using stop strategy and revision mechanisms, we can differentiate pathological images and use auxiliary information to assist in different levels of classification in the field of medical image analysis. The experimental results show that our model is better than some popular multi-granularity hierarchical based few-shot learning models.