Query-guided generalizable medical image segmentation

Abstract

The practical implementation of deep neural networks in clinical settings faces hurdles due to variations in data distribution across different centers. While the incorporation of query-guided Transformer has improved performance across diverse tasks, the full scope of their generalization capabilities remains unexplored. Given the ability of the query-guided Transformer to dynamically adjust to individual samples, fulfilling the need for domain generalization, this paper explores the potential of query-based Transformer for cross-center generalization and introduces a novel Query-based Cross-Center medical image Segmentation mechanism (QuCCeS). By integrating a query-guided Transformer into a U-Net-like architecture, QuCCeS utilizes attribution modeling capability of query-guided Transformer decoder for segmentation in fluctuating scenarios with limited data. Additionally, QuCCeS incorporates an auxiliary task with adaptive sample weighting for coarse mask prediction. Experimental results demonstrate QuCCeS’s superior generalization on unseen domains.