What Appears Appealing May Not be Significant! -- A Clinical Perspective of Diffusion Models

TL;DR

This paper explores methods to evaluate the clinical significance of synthetic polyp images generated by diffusion models, aiming to connect visual quality with clinical relevance in medical imaging.

Contribution

It introduces strategies for assessing clinical significance of synthetic medical images and investigates the correlation between qualitative appearance and clinical importance.

Findings

Proposed evaluation strategies for clinical relevance

Identified challenges in linking visual quality to clinical significance

Explored potential correlations between qualitative and clinical assessments

Abstract

Various trending image generative techniques, such as diffusion models, have enabled visually appealing outcomes with just text-based descriptions. Unlike general images, where assessing the quality and alignment with text descriptions is trivial, establishing such a relation in a clinical setting proves challenging. This work investigates various strategies to evaluate the clinical significance of synthetic polyp images of different pathologies. We further explore if a relation could be established between qualitative results and their clinical relevance.