Background and objective

Early identification of melanoma is conducted through whole-body visual examinations to detect suspicious pigmented lesions, a situation that fluctuates in accuracy depending on the experience and time of the examiner. Computer-aided diagnosis tools for skin lesions are typically trained using pre-selected single-lesion images, taken under controlled conditions, which limits their use in wide-field scenes. Here, we propose a computer-aided classifier system with such input conditions to aid in the rapid identification of suspicious pigmented lesions at the primary care level.

Methods

133 patients with a multitude of skin lesions were recruited for this study. All lesions were examined by a board-certified dermatologist and classified into “suspicious” and “non-suspicious”. A new clinical database was acquired and created by taking Wide-Field images of all major body parts with a consumer-grade camera under natural illumination condition and with a consistent source of image variability. 3–8 images were acquired per patient on different sites of the body, and a total of 1759 pigmented lesions were extracted. A machine learning classifier was optimized and build into a computer aided classification system to binary classify each lesion using a suspiciousness score.

Results

In a testing set, our computer-aided classification system achieved a sensitivity of 100% for suspicious pigmented lesions that were later confirmed by dermoscopy examination (“SPL_A”) and 83.2% for suspicious pigmented lesions that were not confirmed after examination (“SPL_B”). Sensitivity for non-suspicious lesions was 72.1%, and accuracy was 75.9%. With these results we defined a suspiciousness score that is aligned with common macro-screening (naked eye) practices.

Conclusions

This work demonstrates that wide-field photography combined with computer-aided classification systems can distinguish suspicious from non-suspicious pigmented lesions, and might be effective to assess the severity of a suspicious pigmented lesions. We believe this approach could be useful to support skin screenings at a population-level.

中文翻译：

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使用宽视野图像对可疑色素病变进行计算机辅助分类。

背景和目标

黑色素瘤的早期识别是通过全身视觉检查来发现可疑的色素性病变，这种情况的准确性会随着检查者的经验和时间而波动。通常使用在控制条件下拍摄的预先选择的单个病变图像来训练用于皮肤病变的计算机辅助诊断工具，这限制了它们在宽视野场景中的使用。在这里，我们提出一种具有这种输入条件的计算机辅助分类器系统，以帮助在初级保健水平上快速识别可疑色素性病变。

方法

这项研究招募了133名皮肤损伤患者。所有病变均由经董事会认证的皮肤科医生进行检查，分为“可疑”和“非可疑”。通过在自然光照条件下使用一致的图像可变性源，使用消费级相机拍摄所有主要身体部位的广角图像，从而获得并创建了一个新的临床数据库。每位患者在身体的不同部位均采集了3–8张图像，总共提取了1759个色素沉着的病变。对机器学习分类器进行了优化，并将其构建到计算机辅助分类系统中，以使用可疑度分数对每个病变进行二进制分类。

结果

在一个测试集中，我们的计算机辅助分类系统对可疑色素沉着病灶（后来通过皮肤镜检查（“ SPL_A”）确认）的敏感性为100％，对于未经过检查确认的可疑色素沉着病灶（“ SPL_B”）的敏感性为83.2％ ）。非可疑病变的敏感度为72.1％，准确度为75.9％。根据这些结果，我们定义了一个可疑分数，该分数与常见的宏筛选（裸眼）做法一致。

结论

这项工作表明，广角摄影与计算机辅助分类系统的结合可以区分可疑色素病变和非可疑色素病变，并可能有效地评估可疑色素病变的严重程度。我们认为这种方法可能有助于在人群一级进行皮肤筛查。