Neighborhood Self-Dissimilarity Attention for Medical Image Segmentation
Chen Junren, Rui Chen, Gang Liang, Wei Wang, Junlong Cheng, zhanglei-scu, Liangyin Chen
Automated medical image segmentation based on neural networks is pivotal in promoting health equity. The attention mechanism increasingly serves as a key component in modern neural networks for medical image segmentation, as it enables the network to focus on regions of interest, achieving improved segmentation.  However, current attention mechanisms confront a performance-complexity trade-off paradox: accuracy gains demand higher computational costs, while reducing complexity sacrifices model accuracy. Such a contradiction inherently restricts real-world deployment in resource-limited settings, thus exacerbating healthcare disparities.  To overcome this dilemma, we propose a parameter-free Neighborhood Self-Dissimilarity Attention (NSDA), inspired by radiologists' diagnostic patterns of prioritizing regions exhibiting substantial differences during clinical image interpretation.  Unlike pairwise-similarity-based self-attention mechanisms, NSDA constructs a size-adaptive local dissimilarity measure that quantifies element-neighborhood differences. This approach enhances segmentation accuracy without adding trainable parameters directly related to computational complexity.  Experiments demonstrate the effectiveness and generalization of our method. This study advances attention mechanism design for medical image analysis by clinical prior knowledge, a parameter-free paradigm that has the potential to advance health equity in low-resource settings. The code is available upon publication.