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.

翻译：头皮疾病和脱发影响着全球数百万人，凸显了对该疾病进行早期诊断和管理的迫切需求。然而，由于数据不平衡和标注成本高昂带来的挑战，开发一个涵盖这些病症的综合性人工智能诊断系统仍是一个尚未充分探索的领域。为解决这些问题，我们提出了 ScalpVision，一个用于头皮疾病和脱发整体诊断的人工智能驱动系统。在 ScalpVision 中，在缺乏传统头发掩码标签的情况下，我们利用伪图像-标签对和一种创新的提示方法实现了有效的头发分割。该方法对于提取关键特征（如头发厚度和数量）至关重要，这些特征随后被用于评估脱发严重程度。此外，ScalpVision 引入了 DiffuseIT-M，这是一种能够在保持头发信息的同时进行数据集增强的生成模型，有助于改进头皮疾病严重程度的预测。我们的实验结果证实了 ScalpVision 在诊断多种头皮疾病和脱发方面的有效性，展示了其作为皮肤病学护理中一种有价值工具的潜力。