What Appears Appealing May Not be Significant! -- A Clinical Perspective of Diffusion Models
This work addresses the problem of ensuring synthetic medical images are clinically useful for healthcare professionals, though it appears incremental as it focuses on evaluation rather than new model development.
The paper tackles the challenge of evaluating the clinical significance of synthetic polyp images generated by diffusion models, exploring strategies to assess their relevance and alignment with clinical needs beyond visual appeal.
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.