Few-shot Medical Image Segmentation with Cycle-resemblance Attention
This addresses the challenge of segmenting medical images with limited labeled data, which is critical for medical imaging applications due to high annotation costs, though it is an incremental improvement over existing prototype-based methods.
The paper tackles few-shot medical image segmentation by proposing a Cycle-Resemblance Attention module integrated with a prototype network to better capture pixel-wise relations between query and support images, achieving superior performance on abdomen MRI and CT datasets compared to state-of-the-art methods.
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.