Zero-shot Segmentation of Skin Conditions: Erythema with Edit-Friendly Inversion

TL;DR

This paper introduces a zero-shot skin condition segmentation method that uses diffusion models to generate reference images and color analysis to detect erythema, reducing the need for labeled datasets.

Contribution

It presents a novel zero-shot framework combining generative editing and color segmentation for dermatological image analysis.

Findings

Successfully isolated facial erythema in diverse cases

Outperformed baseline threshold-based techniques

Reduced reliance on annotated datasets

Abstract

This study proposes a zero-shot image segmentation framework for detecting erythema (redness of the skin) using edit-friendly inversion in diffusion models. The method synthesizes reference images of the same patient that are free from erythema via generative editing and then accurately aligns these references with the original images. Color-space analysis is performed with minimal user intervention to identify erythematous regions. This approach significantly reduces the reliance on labeled dermatological datasets while providing a scalable and flexible diagnostic support tool by avoiding the need for any annotated training masks. In our initial qualitative experiments, the pipeline successfully isolated facial erythema in diverse cases, demonstrating performance improvements over baseline threshold-based techniques. These results highlight the potential of combining generative diffusion models and statistical color segmentation for computer-aided dermatology, enabling efficient erythema detection without prior training data.