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

Vanshali Sharma

TL;DR

This work tackles the challenge of evaluating the clinical significance of diffusion-model-generated colonoscopy polyp images, specifically distinguishing adenomatous (AD) from non-adenomatous (Non-AD) polyps. It introduces a two-stage stable-diffusion training approach with a denoising U-Net to capture polyp-patterns and pathology labels, followed by assessment of clinical relevance using $t$-SNE, Kernel Inception Distance (KID), and augmentation-based binary classification. Across datasets (SUN, CVC-ClinicHD-Segment, CVC-ClinicHD-Classification), the study finds that visually appealing generations in early iterations do not reliably indicate clinical relevance, and even the best quantitative metrics may not surpass real data for augmentation. The findings underscore the need for dedicated clinical evaluation frameworks when applying medical image generation and point to future directions for aligning synthetic outputs with tangible clinical utility.

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.

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

TL;DR

This work tackles the challenge of evaluating the clinical significance of diffusion-model-generated colonoscopy polyp images, specifically distinguishing adenomatous (AD) from non-adenomatous (Non-AD) polyps. It introduces a two-stage stable-diffusion training approach with a denoising U-Net to capture polyp-patterns and pathology labels, followed by assessment of clinical relevance using -SNE, Kernel Inception Distance (KID), and augmentation-based binary classification. Across datasets (SUN, CVC-ClinicHD-Segment, CVC-ClinicHD-Classification), the study finds that visually appealing generations in early iterations do not reliably indicate clinical relevance, and even the best quantitative metrics may not surpass real data for augmentation. The findings underscore the need for dedicated clinical evaluation frameworks when applying medical image generation and point to future directions for aligning synthetic outputs with tangible clinical utility.

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.
Paper Structure (4 sections, 3 figures, 2 tables)

This paper contains 4 sections, 3 figures, 2 tables.

Table of Contents

  1. Introduction
  2. Methodology
  3. Experiments
  4. Conclusion

Figures (3)

  • Figure 1: The image illustrates the diffusion model training process and assesses synthetic image quality in a clinical setting.
  • Figure 2: From left to right, and continuing the same sequence in the next row, the images correspond to t-SNE plots for iterations from 1k to 8k.
  • Figure 3: Row1: AD, Row2: Non-AD. From left to right, and continuing the same sequence in the next row, the synthetic images correspond to iterations 1k, 4k, and 8k.