Applicability of PPR to other medical image analysis tasks

Investigate whether the Prototype Purification and Regulation (PPR) framework, including its prototype purification and comorbidity-aware prototype regulation mechanisms, can be applied effectively to medical image analysis tasks beyond image-level disease classification, such as lesion localization, segmentation, and prognosis prediction.

Background

The paper develops PPR for multi-label few-shot image-level disease classification in medical image analysis, particularly chest X-ray diagnosis. Its purification mechanism uses sample-level comorbidity information to construct disease-specific prototypes, while its regulation mechanism uses disease-level comorbidity statistics to control inter-class prototype similarity.

The authors explicitly state that PPR’s applicability beyond image-level disease classification has not yet been examined. They identify lesion localization, segmentation, and prognosis prediction as concrete medical image analysis tasks to which the framework might be extended, leaving its effectiveness on those tasks unresolved.

References

Although the proposed purification and regulation mechanisms demonstrate effectiveness in MLFSL, their applicability to other MIA tasks remains unexplored.

— Purification and Regulation: Comorbidity-Aware Multi-Label Few-Shot Learning for Medical Image Classification  (2609.21541 - Lin et al., 18 Sep 2026) in Section Discussion, subsection “Limitations and future works”