Reflectance Confocal Microscopy (RCM) is a non-invasive imaging technique
used in biomedical research and clinical dermatology. It provides virtual
high-resolution images of the skin and superficial tissues, reducing the need
for physical biopsies. RCM employs a laser light source to illuminate the
tissue, capturing the reflected light to generate detailed images of
microscopic structures at various depths. Recent studies explored AI and
machine learning, particularly CNNs, for analyzing RCM images. Our study
proposes a segmentation strategy based on textural features to identify
clinically significant regions, empowering dermatologists in effective image
interpretation and boosting diagnostic confidence. This approach promises to
advance dermatological diagnosis and treatment.

反射共聚焦显微镜（RCM）是一种非侵入性成像技术，用于生物医学研究和临床皮肤学。它提供了皮肤和表浅组织的虚拟高分辨率图像，减少了对物理活检的需求。RCM 采用激光光源照射组织，捕捉反射光生成不同深度微观结构的详细图像。最近的研究探索了 AI 和机器学习，特别是 CNN，用于分析 RCM 图像。本研究提出了一种基于纹理特征的分割策略，用于识别具有临床意义的区域，提升皮肤科医生的图像解释能力和诊断信心。这种方法有望推动皮肤病的诊断和治疗。

通过利用人工智能驱动的反射共聚焦显微镜分析提升诊断

Enhancing Diagnosis through AI-driven Analysis of Reflectance Confocal  Microscopy

While deep learning based approaches have demonstrated expert-level
performance in dermatological diagnosis tasks, they have also been shown to
exhibit biases toward certain demographic attributes, particularly skin types
(e.g., light versus dark), a fairness concern that must be addressed. We
propose CIRCLe, a skin color invariant deep representation learning method for
improving fairness in skin lesion classification. CIRCLe is trained to classify
images by utilizing a regularization loss that encourages images with the same
diagnosis but different skin types to have similar latent representations.
Through extensive evaluation and ablation studies, we demonstrate CIRCLe's
superior performance over the state-of-the-art when evaluated on 16k+ images
spanning 6 Fitzpatrick skin types and 114 diseases, using classification
accuracy, equal opportunity difference (for light versus dark groups), and
normalized accuracy range, a new measure we propose to assess fairness on
multiple skin type groups.

本论文提出了一种基于 CIRCLe 的深度表示学习算法，通过添加正则化损失函数实现在皮肤类型不同的图像表现出相似的潜在表示，从而提高皮损分类的公平性，并在 16k + 张图像上进行了评估和消融研究，证明了其在使用分类准确性，对浅色与深色人群的平等机会差异和标准化准确性范围（一种新的公平性评估方法）上优于现有技术水平的表现。

CIRCLe：用于皮肤病变无偏分类的颜色不变表示学习

CIRCLe: Color Invariant Representation Learning for Unbiased  Classification of Skin Lesions

Skin lesion identification is a key step toward dermatological diagnosis.
When describing a skin lesion, it is very important to note its body site
distribution as many skin diseases commonly affect particular parts of the
body. To exploit the correlation between skin lesions and their body site
distributions, in this study, we investigate the possibility of improving skin
lesion classification using the additional context information provided by body
location. Specifically, we build a deep multi-task learning (MTL) framework to
jointly optimize skin lesion classification and body location classification
(the latter is used as an inductive bias). Our MTL framework uses the
state-of-the-art ImageNet pretrained model with specialized loss functions for
the two related tasks. Our experiments show that the proposed MTL based method
performs more robustly than its standalone (single-task) counterpart.

使用深度多任务学习（MTL）框架，结合图片的身体部位信息和 ImageNet 预训练模型，实现皮肤病变分类和身体部位分类的联合优化，结果表明该方法比独立单任务表现更为鲁棒。