Accurate skin diagnosis through end-user applications is important for early detection and cure of severe skin diseases. However, the low quality of dermoscopic images hampers this mission, especially with the presence of hair on these kinds of images. This paper introducesDM–AHR, a novel, self-supervised conditional diffusion model designed specifically for the automatic generation of hairless dermoscopic images to improve the quality of skin diagnosis applications. The current research contributes in three significant ways to the field of dermatologic imaging. First, we develop a customized diffusion model that adeptly differentiates between hair and skin features. Second, we pioneer a novel self-supervised learning strategy that is specifically tailored to optimize performance for hairless imaging. Third, we introduce a new dataset, namedDERMAHAIR(DERMatologic Automatic HAIR Removal Dataset), that is designed to advance and benchmark research in this specialized domain. These contributions significantly enhance the clarity of dermoscopic images, improving the accuracy of skin diagnosis procedures. We elaborate on the architecture ofDM–AHRand demonstrate its effective performance in removing hair while preserving critical details of skin lesions. Our results show an enhancement in the accuracy of skin lesion analysis when compared to existing techniques. Given its robust performance,DM–AHRholds considerable promise for broader application in medical image enhancement.
通过终端用户应用实现准确的皮肤诊断对于严重皮肤疾病的早期发现与治疗至关重要。然而，皮肤镜图像的低质量阻碍了这一使命的实现，特别是在此类图像存在毛发的情况下。本文提出DM-AHR——一种新颖的自监督条件扩散模型，专门设计用于自动生成无毛发皮肤镜图像，以提升皮肤诊断应用的质量。本研究在皮肤影像学领域做出了三个重要贡献：首先，我们开发了一种能够精准区分毛发与皮肤特征的定制化扩散模型；其次，我们开创了一种专门针对无毛发成像优化的新型自监督学习策略；第三，我们构建了名为DERMAHAIR（皮肤镜自动毛发去除数据集）的全新数据集，旨在推动该专业领域的研究并建立性能基准。这些贡献显著提升了皮肤镜图像的清晰度，从而提高了皮肤诊断流程的准确性。我们详细阐述了DM-AHR的架构设计，并证明其在去除毛发的同时能有效保留皮肤病灶关键细节的卓越性能。实验结果表明，与现有技术相比，我们的方法显著提升了皮肤病灶分析的准确性。鉴于其稳健的性能表现，DM-AHR在医学图像增强领域具有广阔的应用前景。
肿瘤(癌症)患者之家