Scalp Diagnostic System With Label-Free Segmentation and Training-Free Image Translation

TL;DR

ScalpVision is an AI system that enables label-free segmentation and training-free image translation to improve diagnosis of scalp diseases, addressing data imbalance and annotation challenges in dermatology.

Contribution

The paper introduces ScalpVision, a novel AI system that performs effective hair segmentation and dataset augmentation without traditional labels or training, enhancing scalp disease diagnosis.

Findings

Effective hair segmentation using pseudo labels and prompting.

Improved disease severity prediction through DiffuseIT-M augmentation.

Validated efficiency in diagnosing various scalp conditions.

Abstract

Scalp disorders are highly prevalent worldwide, yet remain underdiagnosed due to limited access to expert evaluation and the high cost of annotation. Although AI-based approaches hold great promise, their practical deployment is hindered by challenges such as severe data imbalance and the absence of pixel-level segmentation labels. To address these issues, we propose ScalpVision, an AI-driven system for the holistic diagnosis of scalp diseases. In ScalpVision, effective hair segmentation is achieved using pseudo image-label pairs and an innovative prompting method in the absence of traditional hair masking labels. Additionally, ScalpVision introduces DiffuseIT-M, a generative model adopted for dataset augmentation while maintaining hair information, facilitating improved predictions of scalp disease severity. Our experimental results affirm ScalpVision's efficiency in diagnosing a variety of scalp conditions, showcasing its potential as a valuable tool in dermatological care. Our code is available at https://github.com/winston1214/ScalpVision.