用生成模型提升皮肤癌诊断中低肤色人群的公平性
DermDiff: Generative Diffusion Model for Mitigating Racial Biases in Dermatology Diagnosis
- 通过文本引导生成多样化皮科图像数据
- 显著提升低频肤色群体样本的多样性与保真度
- 适合关注医疗AI公平性的研究者与临床开发者
皮肤疾病（如皮肤癌）是重大公共卫生问题，早期诊断对治疗至关重要。人工智能算法有望辅助区分良恶性皮肤病变并提升诊断准确率。然而，现有皮肤疾病诊断AI模型多基于有限且存在偏见的数据集训练和测试，导致在特定肤色群体上表现不佳。为此，我们提出一种新型生成模型DermDiff，可生成多样且具有代表性的皮肤镜图像数据，用于皮肤疾病诊断。该模型利用文本提示与多模态图文学习，增强高度不平衡数据集中代表性不足群体（患者、疾病等）的表征能力。大量实验证明，DermDiff在图像保真度与多样性方面表现优异。下游评估进一步表明，该模型在缓解皮肤病学诊断中的种族偏见方面具有潜力。代码已公开于 https://github.com/Munia03/DermDiff。
原文摘要 · Abstract (English)
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
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