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.

翻译：近年来，基于卷积神经网络（CNN）的深度学习在许多应用中取得了显著成功。然而，它们对大量标注数据的严重依赖以及对未见类别的有限泛化能力，给其在医学图像处理任务中的适用性带来了挑战。小样本学习利用少量标注数据泛化到未见类别，已成为一个关键研究领域并受到广泛关注。目前，大多数研究采用基于原型的方法，即使用原型网络从支持集构建原型，以指导查询集的处理并获取最终结果。尽管有效，但这种方法过度依赖支持集而忽略了查询集，导致模型类别内存在显著差异。为缓解这一缺陷，我们提出了一种新颖的支持-查询原型融合网络（SQPFNet）。SQPFNet首先为支持图像的多个前景区域生成支持原型，从而产生粗分割掩码。随后，基于该粗分割掩码构建查询原型，额外利用查询集中的模式信息。因此，SQPFNet构建了高质量的支持-查询融合原型，并以此对查询图像进行分割，获得最终精细化的查询掩码。在两个公开数据集SABS和CMR上的评估结果表明，SQPFNet达到了最先进的性能。