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

arXiv: https://arxiv.org/abs/2508.01334 · HTML: https://mlsanity.com/paper/2508.01334/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Placed in Medical imaging and segmentation, one of 14 directions in Diffusion models.
- First posted on arXiv 2 Aug 2025.

## Placed in

- Diffusion models / [Medical imaging and segmentation](https://mlsanity.com/diffusion/medical-imaging-and-segmentation/)

## Built on by (most cited first, 1 of 1)

- [Zero-Shot Generative De-identification: Inversion-Free Flow for Privacy-Preserving Skin Image Analysis](https://mlsanity.com/paper/2602.00821/) (2026)

## Abstract (arXiv)

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.