  Recently, due to the increasing requirements of medical imaging applications
and the professional requirements of annotating medical images, few-shot
learning has gained increasing attention in the medical image semantic
segmentation field. To perform segmentation with limited number of labeled
medical images, most existing studies use Proto-typical Networks (PN) and have
obtained compelling success. However, these approaches overlook the query image
features extracted from the proposed representation network, failing to
preserving the spatial connection between query and support images. In this
paper, we propose a novel self-supervised few-shot medical image segmentation
network and introduce a novel Cycle-Resemblance Attention (CRA) module to fully
leverage the pixel-wise relation between query and support medical images.
Notably, we first line up multiple attention blocks to refine more abundant
relation information. Then, we present CRAPNet by integrating the CRA module
with a classic prototype network, where pixel-wise relations between query and
support features are well recaptured for segmentation. Extensive experiments on
two different medical image datasets, e.g., abdomen MRI and abdomen CT,
demonstrate the superiority of our model over existing state-of-the-art
methods.
