Multi-Level Feature-Guided Network for Few-shot Medical Image Segmentation

Few-shot medical image segmentation is a prominent area of collaborative medical technology research to support healthcare and homecare, but often grapples with challenges, such as the problem of local information loss in most of the current prototype-based methods. To mitigate this concern, we propose a novel Multi-Level Feature-Guided Network (MLFGNet) for few-shot medical image segmentation, which aims to enhance segmentation performance by extracting richer information from multi-level features. Specifically, A Multi-Feature Processing Module is proposed that utilizes hybrid attention mechanisms working together to efficiently learn more useful features. Furthermore, we design a Prediction Aggregation Module to aggregate multiple segmentation prediction maps by assigning appropriate weights, thereby effectively improving segmentation accuracy. Extensive experiments prove that our method exhibits favorable performance with respect to the state-of-the-art methods on two public datasets, including Abdominal-CT and Abdominal-MRI datasets.