Deep learning has achieved tremendous success in computer vision, while medical image segmentation (MIS) remains a challenge, due to the scarcity of data annotations. Meta-learning techniques for few-shot segmentation (Meta-FSS) have been widely used to tackle this challenge, while they neglect possible distribution shifts between the query image and the support set. In contrast, an experienced clinician can perceive and address such shifts by borrowing information from the query image, then fine-tune or calibrate her prior cognitive model accordingly. Inspired by this, we propose Q-Net, a Query-informed Meta-FSS approach, which mimics in spirit the learning mechanism of an expert clinician. We build Q-Net based on ADNet, a recently proposed anomaly detection-inspired method. Specifically, we add two query-informed computation modules into ADNet, namely a query-informed threshold adaptation module and a query-informed prototype refinement module. Combining them with a dual-path extension of the feature extraction module, Q-Net achieves state-of-the-art performance on widely used abdominal and cardiac magnetic resonance (MR) image datasets. Our work sheds light on a novel way to improve Meta-FSS techniques by leveraging query information.

翻译：深度学习在计算机视觉领域取得了巨大成功，然而由于数据标注的稀缺性，医学图像分割（MIS）仍然是一项挑战。针对小样本分割的元学习技术（Meta-FSS）已被广泛用于应对这一挑战，但这类方法忽略了查询图像与支持集之间可能存在的分布偏移。相比之下，经验丰富的临床医生能够通过从查询图像中借鉴信息来感知并解决此类偏移，从而相应地对自身先验认知模型进行微调或校准。受此启发，我们提出Q-Net——一种查询信息驱动的元小样本学习方法，其本质模拟了专家临床医生的学习机制。我们基于ADNet（一种近期提出的基于异常检测的方法）构建Q-Net。具体而言，我们在ADNet中添加了两个查询信息驱动的计算模块，即查询信息驱动的阈值自适应模块和查询信息驱动的原型精炼模块。通过将它们与特征提取模块的双路径扩展相结合，Q-Net在广泛使用的腹部和心脏磁共振（MR）图像数据集上达到了最先进的性能。我们的研究揭示了通过利用查询信息改进元小样本分割技术的一种新途径。