论文标题
超级像素的自我判断：训练少量射击医学图像分割而无需注释
Self-Supervision with Superpixels: Training Few-shot Medical Image Segmentation without Annotation
论文作者
论文摘要
很少有射击语义分割（FSS）具有医学成像应用的巨大潜力。大多数现有的FSS技术都需要大量的注释语义课程进行培训。但是，由于缺乏注释，这些方法可能不适用于医疗图像。为了解决这个问题，我们做出了一些贡献：（1）为医学图像的新型自我监督FSS框架，以消除培训期间注释的要求。此外，生成基于超像素的伪标签以提供监督。 （2）插入原型网络中的自适应本地原型池池池池，以解决医疗图像细分中常见的挑战性前景不平衡问题； （3）我们使用三个不同的任务证明了对医学图像的一般适用性：用于CT和MRI的腹部器官分割以及MRI的心脏分割。我们的结果表明，对于医疗图像进行分割，提出的方法优于常规FSS方法，这些方法需要手动注释进行培训。
Few-shot semantic segmentation (FSS) has great potential for medical imaging applications. Most of the existing FSS techniques require abundant annotated semantic classes for training. However, these methods may not be applicable for medical images due to the lack of annotations. To address this problem we make several contributions: (1) A novel self-supervised FSS framework for medical images in order to eliminate the requirement for annotations during training. Additionally, superpixel-based pseudo-labels are generated to provide supervision; (2) An adaptive local prototype pooling module plugged into prototypical networks, to solve the common challenging foreground-background imbalance problem in medical image segmentation; (3) We demonstrate the general applicability of the proposed approach for medical images using three different tasks: abdominal organ segmentation for CT and MRI, as well as cardiac segmentation for MRI. Our results show that, for medical image segmentation, the proposed method outperforms conventional FSS methods which require manual annotations for training.
