用局部图像块提升玫瑰痤疮自动检测准确率与隐私保护
Patch-based Automatic Rosacea Detection Using the ResNet Deep Learning Framework
- 从面部图像提取多尺寸、多位置的局部图像块进行分析
- 局部图像块策略在准确率和敏感度上优于全图方法
- 仅使用局部区域，天然保护患者隐私，提升模型可解释性
玫瑰痤疮是一种慢性炎症性皮肤病，表现为面部红斑、丘疹和可见毛细血管，早期精准检测可显著提高治疗效果。本文提出基于ResNet-18深度学习框架的新型分块式自动玫瑰痤疮检测方法。首先，从不同人群的面部图像中提取多种尺寸、形状和位置的图像块；其次，通过多项实验研究局部视觉信息对深度学习模型性能的影响；第三，大量实验证明，多种基于图像块的检测策略在准确率和敏感度上达到或超过全图方法；最后，所提策略仅使用局部图像块，无需包含可识别面部特征，天然保护患者隐私。实验结果表明，该方法引导模型聚焦临床相关区域，增强鲁棒性与可解释性，为自动化皮肤科诊断提供实用参考。
原文摘要 · Abstract (English)
Rosacea, which is a chronic inflammatory skin condition that manifests with facial redness, papules, and visible blood vessels, often requirs precise and early detection for significantly improving treatment effectiveness. This paper presents new patch-based automatic rosacea detection strategies using the ResNet-18 deep learning framework. The contributions of the proposed strategies come from the following aspects. First, various image pateches are extracted from the facial images of people in different sizes, shapes, and locations. Second, a number of investigation studies are carried out to evaluate how the localized visual information influences the deep learing model performance. Third, thorough experiments are implemented to reveal that several patch-based automatic rosacea detection strategies achieve competitive or superior accuracy and sensitivity than the full-image based methods. And finally, the proposed patch-based strategies, which use only localized patches, inherently preserve patient privacy by excluding any identifiable facial features from the data. The experimental results indicate that the proposed patch-based strategies guide the deep learning model to focus on clinically relevant regions, enhance robustness and interpretability, and protect patient privacy. As a result, the proposed strategies offer practical insights for improving automated dermatological diagnostics.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。