تقرير
Scalp Diagnostic System With Label-Free Segmentation and Training-Free Image Translation
| العنوان: | Scalp Diagnostic System With Label-Free Segmentation and Training-Free Image Translation |
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| المؤلفون: | Kim, Youngmin, Kim, Saejin, Moon, Hoyeon, Yu, Youngjae, Noh, Junhyug |
| سنة النشر: | 2024 |
| المجموعة: | Computer Science |
| مصطلحات موضوعية: | Computer Science - Computer Vision and Pattern Recognition |
| الوصف: | Scalp diseases and alopecia 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 and alopecia. 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. This approach is crucial for extracting key features such as hair thickness and count, which are then used to assess alopecia severity. Additionally, ScalpVision introduces DiffuseIT-M, a generative model adept at 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 and alopecia, showcasing its potential as a valuable tool in dermatological care. Comment: IEEE Transactions on Medical Imaging (Under Review) |
| نوع الوثيقة: | Working Paper |
| URL الوصول: | http://arxiv.org/abs/2406.17254 |
| رقم الأكسشن: | edsarx.2406.17254 |
| قاعدة البيانات: | arXiv |
| الوصف غير متاح. |