基于白化余弦相似度的可解释性玫瑰痤疮自动检测
Interpretable Automatic Rosacea Detection with Whitened Cosine Similarity
AI总结：
本文提出一种基于白化余弦相似度的可解释性玫瑰痤疮自动检测方法，通过比较测试样本与两类均值的相似度，在保证高准确率的同时提升结果可解释性，并公开代码与数据。
摘要：
根据美国国家玫瑰痤疮协会的数据，大约有1600万美国人患有玫瑰痤疮，这是一种常见的皮肤疾病，会导致面部潮红或长期发红。为了提高人们对玫瑰痤疮的认识，并更好地帮助医生对该疾病进行诊断，本文提出了一种基于白化余弦相似度的可解释性玫瑰痤疮自动检测方法。所提方法的贡献有三方面。第一，该方法能够在未见过的测试数据上自动区分玫瑰痤疮患者与未患该疾病的人群，其准确率显著高于其他方法，包括经典的深度学习和统计方法。第二，该方法通过测量测试样本与两类均值之间的相似度来解决可解释性问题，即玫瑰痤疮类与正常类之间的相似度，这使得医疗专业人员和患者都能理解并信任检测结果。最后，所提方法不仅有助于提高普通人群对玫瑰痤疮的认识，还将有助于提醒患有该疾病的患者尽早接受治疗，因为玫瑰痤疮在早期阶段更容易治疗。代码和数据可在 https://github.com/chengyuyang-njit/ICCRD-2025 获取。代码和数据可在 https://github.com/chengyuyang-njit/ICCRD-2025 获取。
英文摘要：
According to the National Rosacea Society, approximately sixteen million Americans suffer from rosacea, a common skin condition that causes flushing or long-term redness on a person's face. To increase rosacea awareness and to better assist physicians to make diagnosis on this disease, we propose an interpretable automatic rosacea detection method based on whitened cosine similarity in this paper. The contributions of the proposed methods are three-fold. First, the proposed method can automatically distinguish patients suffering from rosacea from people who are clean of this disease with a significantly higher accuracy than other methods in unseen test data, including both classical deep learning and statistical methods. Second, the proposed method addresses the interpretability issue by measuring the similarity between the test sample and the means of two classes, namely the rosacea class versus the normal class, which allows both medical professionals and patients to understand and trust the results. And finally, the proposed methods will not only help increase awareness of rosacea in the general population, but will also help remind patients who suffer from this disease of possible early treatment, as rosacea is more treatable in its early stages. The code and data are available at https://github.com/chengyuyang-njit/ICCRD-2025. The code and data are available at https://github.com/chengyuyang-njit/ICCRD-2025.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
