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

Fuente: arXiv
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Main Author: Sharma, Vanshali
Format: Preprint
Published: 2024
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author Sharma, Vanshali
author_facet Sharma, Vanshali
contents 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.
format Preprint
id arxiv_https___arxiv_org_abs_2407_10029
institution arXiv
publishDate 2024
record_format arxiv
spellingShingle What Appears Appealing May Not be Significant! -- A Clinical Perspective of Diffusion Models
Sharma, Vanshali
Computer Vision and Pattern Recognition
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.
title What Appears Appealing May Not be Significant! -- A Clinical Perspective of Diffusion Models
topic Computer Vision and Pattern Recognition
url https://arxiv.org/abs/2407.10029