Learning Query-assisted Multiple Prototypes for Few-shot Medical Image Segmentation

Few-shot semantic segmentation, which is dedicated to the generalization of models to segment novel classes with scarce annotated samples, has achieved tremendous progress recently due to the significant advancement of deep CNNs. However, existing approaches in medical scenarios, namely Few-Shot Medical Image Segmentation (FSMIS), still encounter two primary obstacles. First, there exists huge appearance discrepancy between support and query images, which hinders the knowledge transferring and adversely affects segmentation performance. Second, almost all current prototype-based methods struggle to learn and optimize limited support prototypes, giving insufficient attention to query information, which makes it challenging to achieve high-quality query segmentation. Consequently, we propose a novel Query-Assisted Multiple Prototypes (QAMP) approach, where in addition to normal support prototypes, query prototypes are additionally mined leveraging high-confidence initial query predictions. Specifically, we design a Query Prior Generation (QPG) module to locate positions where query objects may belong to with high confidence. Subsequently, based on corresponding support mask and query prior, a Mask Guided Support Prototypes (MGSP) module and a Prior Guided Query Prototypes (PGQP) module are employed to generate support and query prototypes respectively, which can effectively capture underlying characteristics of the query targets. Extensive experiments and visualization on three publicly available medical image datasets demonstrate the superiority of our QAMP compared with current methods. Code is available at https://github.com/jcjiang99/QAMP .