DermDiff：用于缓解皮肤病诊断中种族偏见的生成式扩散模型
DermDiff: Generative Diffusion Model for Mitigating Racial Biases in Dermatology Diagnosis
AI总结：
针对现有皮肤病诊断AI模型因数据集偏见在部分肤色上表现差的问题，提出生成式扩散模型DermDiff，借助文本提示和多模态图文学习生成多样化皮肤镜图像，改善不平衡数据集的群体表征，可有效缓解皮肤病诊断中的种族偏见。
摘要：
皮肤癌等皮肤疾病是重大公共卫生问题，早期诊断对有效治疗至关重要。人工智能（AI）算法有望辅助对良性与恶性皮肤病变进行分诊，并提高诊断准确率。然而，现有的皮肤病诊断AI模型通常在有限且存在偏见的数据集上开发和测试，导致在某些肤色上的表现不佳。为解决这一问题，我们提出了一种名为DermDiff的新型生成模型，它能够生成多样化且具代表性的皮肤病诊断用皮肤镜图像数据。借助文本提示和多模态图文学习，DermDiff改善了高度不平衡数据集中代表性不足群体（患者、疾病等）的表征。我们的大量实验证明了DermDiff在高保真度和多样性方面的有效性。此外，下游评估表明DermDiff在缓解皮肤病诊断中的种族偏见方面具有潜力。我们的代码可在https://github.com/Munia03/DermDiff获取。
英文摘要：
Skin diseases, such as skin cancer, are a significant public health issue, and early diagnosis is crucial for effective treatment. Artificial intelligence (AI) algorithms have the potential to assist in triaging benign vs malignant skin lesions and improve diagnostic accuracy. However, existing AI models for skin disease diagnosis are often developed and tested on limited and biased datasets, leading to poor performance on certain skin tones. To address this problem, we propose a novel generative model, named DermDiff, that can generate diverse and representative dermoscopic image data for skin disease diagnosis. Leveraging text prompting and multimodal image-text learning, DermDiff improves the representation of underrepresented groups (patients, diseases, etc.) in highly imbalanced datasets. Our extensive experimentation showcases the effectiveness of DermDiff in terms of high fidelity and diversity. Furthermore, downstream evaluation suggests the potential of DermDiff in mitigating racial biases for dermatology diagnosis. Our code is available at https://github.com/Munia03/DermDiff
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。
