Enhanced Slicing Prototype and Hybrid Metric Transformer for Few-shot Medical Image Classification

As one of the most popular neural network modules, Transformer plays a key role in many fundamental deep learning models such as few-shot medical image segmentation, which aims to segment the target objects in query under the condition of a few annotated support images. Most previous works strive to mine more semantically effective information from the support to match with the corresponding objects in query. The traditional models generally input the whole image into the deep neural network to obtain the feature representation, and use only one measurement method to improve efficiency. If the objects in them show large intra-class diversity, the discrepancy gap between query and support images is ignored. To solve this problem, we propose an enhanced slicing prototype and multidimensional metric mechanism to address the inefficiency of existing few-shot learning methods in medical image classification. Instead of whole image is input into the deep neural network, our proposed model segments the image into slices, and then use the self-attention mechanism to generate enhanced feature vectors based on transformer. And then, a hybrid metric is used to measure similarity between features by calculating the distance between the support set and query set slice prototypes to improve efficiency. Experiments demonstrate that our model has better classification effect on mini-MedMNIST, which is a few-shot medical image dataset constructed from MedMNIST dataset.