Context-Fusion Framework Enhances Frozen VLM for Endoscopic Polyp Reporting
A recent submission on arXiv (2608.15580v1) presents a context-fusion framework designed to adapt a frozen general-purpose vision-language model (VLM) for endoscopic polyp reporting without altering its pretrained weights. This framework consolidates quantitative lesion measurements, standardized Paris classification, and meaningful morphological descriptions into a cohesive record. It employs a self-supervised polyp encoder to extract relevant image-report pairs as explicit, query-specific evidence, alongside learned continuous specialist tokens for implicit instructional context. By maintaining the VLM's unified interface, this method introduces reliable specialized knowledge, overcoming the drawbacks of current specialization techniques that depend on task-specific models or weight modifications. The study highlights the potential for enhancing accuracy and efficiency in clinical endoscopic reporting through the intersection of artificial intelligence and medical imaging.
Key facts
- Paper arXiv:2608.15580v1 introduces a context-fusion framework for endoscopic polyp reporting.
- The framework specializes a frozen general-purpose VLM without modifying its pretrained weights.
- It integrates lesion sizing, Paris classification, and morphological description into a single record.
- A self-supervised polyp encoder retrieves related image-report pairs as explicit evidence.
- Learned continuous specialist tokens provide implicit instruction context.
- The approach preserves the VLM's unified interface and pretrained capabilities.
- It addresses limitations of existing specialization strategies that use task-specific models or weight adaptation.
- The paper was announced as a new submission on arXiv.
Entities
Institutions
- arXiv