  In recent years, deep learning based on Convolutional Neural Networks (CNNs)
has achieved remarkable success in many applications. However, their heavy
reliance on extensive labeled data and limited generalization ability to unseen
classes pose challenges to their suitability for medical image processing
tasks. Few-shot learning, which utilizes a small amount of labeled data to
generalize to unseen classes, has emerged as a critical research area,
attracting substantial attention. Currently, most studies employ a
prototype-based approach, in which prototypical networks are used to construct
prototypes from the support set, guiding the processing of the query set to
obtain the final results. While effective, this approach heavily relies on the
support set while neglecting the query set, resulting in notable disparities
within the model classes. To mitigate this drawback, we propose a novel
Support-Query Prototype Fusion Network (SQPFNet). SQPFNet initially generates
several support prototypes for the foreground areas of the support images, thus
producing a coarse segmentation mask. Subsequently, a query prototype is
constructed based on the coarse segmentation mask, additionally exploiting
pattern information in the query set. Thus, SQPFNet constructs high-quality
support-query fused prototypes, upon which the query image is segmented to
obtain the final refined query mask. Evaluation results on two public datasets,
SABS and CMR, show that SQPFNet achieves state-of-the-art performance.
