Tied Prototype Model for Few-Shot Medical Image Segmentation
This work provides an incremental improvement for medical image segmentation researchers by enhancing prototype-based few-shot learning methods.
The paper tackled the problem of few-shot medical image segmentation by proposing the Tied Prototype Model (TPM), which addresses limitations in existing methods like ADNet by using tied prototypes for foreground and background, extending to multiple prototypes and multi-class segmentation, and incorporating adaptive thresholds, resulting in improved segmentation accuracy.
Common prototype-based medical image few-shot segmentation (FSS) methods model foreground and background classes using class-specific prototypes. However, given the high variability of the background, a more promising direction is to focus solely on foreground modeling, treating the background as an anomaly -- an approach introduced by ADNet. Yet, ADNet faces three key limitations: dependence on a single prototype per class, a focus on binary classification, and fixed thresholds that fail to adapt to patient and organ variability. To address these shortcomings, we propose the Tied Prototype Model (TPM), a principled reformulation of ADNet with tied prototype locations for foreground and background distributions. Building on its probabilistic foundation, TPM naturally extends to multiple prototypes and multi-class segmentation while effectively separating non-typical background features. Notably, both extensions lead to improved segmentation accuracy. Finally, we leverage naturally occurring class priors to define an ideal target for adaptive thresholds, boosting segmentation performance. Taken together, TPM provides a fresh perspective on prototype-based FSS for medical image segmentation. The code can be found at https://github.com/hjk92g/TPM-FSS.