Aligned Patch Calibration Attention Network for Few-Shot Medical Image Segmentation

In medical imaging, segmentation of tissues and organs helps doctors accurately determine disease subtypes and propose treatment plans. Few-shot learning, which can be trained with a small amount of annotated data and has better generalization, has achieved excellent results in medical image segmentation, where large-scale annotated data is scarce. However, most current few-shot segmentation models rely on the foreground part of support features, establishing spatial information connections with query features through the foreground part and ignoring the differences in foreground classes between query and support images. Therefore, we propose a novel few-shot segmentation model based on cross-attention: aligned patch calibration attention network (APCANet), which filters out pixels detrimental to segmentation based on cross-attention between support features and query features. Specifically, our model is patch-based, setting the query patches as Query and the aligned support patches as Key and Value for cross-attention. Then, in the cross-attention matrix, by distinguishing the tokens in the patches, we set the weights of non-compliant tokens in the attention matrix to negative infinity, thereby calibrating the harmful pixels in the support features. Additionally, we generate a prior mask for the query image and concatenate it with the query features. Subsequently, we divide the support and query features into patches and align foreground and background patches based on similarity. These operations can better establish spatial correlation between support and query features. The experimental results on the Abd-CT, Card-MRI, and Abd-MRI datasets demonstrate the effectiveness of our method.Code is released at: https://github.com/HengTaoDing/APCANet