用于少样本医学图像分割的支持-查询原型融合网络
Support-Query Prototype Fusion Network for Few-shot Medical Image Segmentation
AI总结：
针对少样本医学图像分割中现有原型方法重支持集、轻查询集导致类内差异大的问题，提出支持-查询原型融合网络SQPFNet，通过融合双原型实现精细化分割，在两个公开数据集上达到SOTA性能。
摘要：
近年来，基于卷积神经网络（CNNs）的深度学习已在诸多应用中取得显著成效。然而，这类方法严重依赖大量标注数据，且对未见过的类别泛化能力有限，这给它们在医学图像处理任务中的适用性带来了挑战。少样本学习利用少量标注数据即可泛化至未见过的类别，已成为重要研究方向，受到广泛关注。目前，多数研究采用基于原型的方法，即利用原型网络从支持集构建原型，引导查询集的处理以得到最终结果。该方法虽有效，但严重依赖支持集而忽略查询集，导致模型类别内部存在显著差异。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首先为支持图像的前景区域生成若干支持原型，进而得到粗分割掩码。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。由此，SQPFNet构建出高质量的支持-查询融合原型，再基于该原型对查询图像进行分割，得到最终的精细化查询掩码。在SABS和CMR两个公开数据集上的评估结果表明，SQPFNet取得了当前最优性能。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
