Most uses of Meta-Learning in visual recognition are very often applied to image classification, with a relative lack of works in other tasks {such} as segmentation and detection. We propose a generic Meta-Learning framework for few-shot weakly-supervised segmentation in medical imaging domains. We conduct a comparative analysis of meta-learners from distinct paradigms adapted to few-shot image segmentation in different sparsely annotated radiological tasks. The imaging modalities include 2D chest, mammographic and dental X-rays, as well as 2D slices of volumetric tomography and resonance images. Our experiments consider a total of 9 meta-learners, 4 backbones and multiple target organ segmentation tasks. We explore small-data scenarios in radiology with varying weak annotation styles and densities. Our analysis shows that metric-based meta-learning approaches achieve better segmentation results in tasks with smaller domain shifts in comparison to the meta-training datasets, while some gradient- and fusion-based meta-learners are more generalizable to larger domain shifts.

翻译：元学习在视觉识别中的大多数应用聚焦于图像分类，在分割与检测等其他任务中相对匮乏。我们提出了一种通用元学习框架，用于医学影像领域的少样本弱监督分割。针对不同稀疏标注的放射学任务，我们对源自不同范式的元学习器进行了比较分析，将其适配于少样本图像分割。影像模态包括二维胸部X光片、乳腺X光片、牙科X光片，以及体积断层扫描和共振图像的二维切片。实验共涉及9种元学习器、4种骨干网络及多个靶器官分割任务。我们探讨了放射学场景下不同弱标注风格与密度的小数据情境。分析表明，基于度量的元学习方法在与元训练数据集域偏移较小的任务中取得了更好的分割效果，而部分基于梯度与融合的元学习器在应对较大域偏移时具有更强的泛化能力。