Few Shot Medical Image Segmentation with Cross Attention Transformer
This addresses the challenge of expensive manual annotation in medical imaging by enabling segmentation from few examples, though it is an incremental improvement in few-shot learning for this domain.
The paper tackles the problem of medical image segmentation with limited annotated data by proposing CAT-Net, a few-shot learning framework using cross masked attention Transformer, which achieves superior performance on three public datasets compared to state-of-the-art methods.
Medical image segmentation has made significant progress in recent years. Deep learning-based methods are recognized as data-hungry techniques, requiring large amounts of data with manual annotations. However, manual annotation is expensive in the field of medical image analysis, which requires domain-specific expertise. To address this challenge, few-shot learning has the potential to learn new classes from only a few examples. In this work, we propose a novel framework for few-shot medical image segmentation, termed CAT-Net, based on cross masked attention Transformer. Our proposed network mines the correlations between the support image and query image, limiting them to focus only on useful foreground information and boosting the representation capacity of both the support prototype and query features. We further design an iterative refinement framework that refines the query image segmentation iteratively and promotes the support feature in turn. We validated the proposed method on three public datasets: Abd-CT, Abd-MRI, and Card-MRI. Experimental results demonstrate the superior performance of our method compared to state-of-the-art methods and the effectiveness of each component. Code: https://github.com/hust-linyi/CAT-Net.