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

Youngmin Kim

Abstract:

Dermatoses of the scalp affect millions of people around the world, underscoring the urgent need for early diagnosis and management of the disease. However, the development of a comprehensive AI-based diagnosis system encompassing these conditions remains an underexplored domain due to the challenges associated with data imbalance and the costly nature of labeling. 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}{https://github.com/winston1214/ScalpVision.