One commonly used clinical approach towards detecting melanomas recognises the existence of Ugly Duckling nevi, or skin lesions which look different from the other lesions on the same patient. An automatic method of detecting and analysing these lesions would help to standardize studies, compared with manual screening methods. However, it is difficult to obtain expertly-labelled images for ugly duckling lesions. We therefore propose to use self-supervised machine learning to automatically detect outlier lesions. We first automatically detect and extract all the lesions from a wide-field skin image, and calculate an embedding for each detected lesion in a patient image, based on automatically identified features. These embeddings are then used to calculate the L2 distances as a way to measure dissimilarity. Using this deep learning method, Ugly Ducklings are identified as outliers which should deserve more attention from the examining physician. We evaluate through comparison with dermatologists, and achieve a sensitivity rate of 72.1% and diagnostic accuracy of 94.2% on the held-out test set.
翻译:一种常见的临床方法来检测乳腺瘤。一种常见的临床方法承认存在与同一病人其他损伤不同的丑鸭阴部或皮肤损伤。一种自动的检测和分析这些损伤的方法将有助于与人工筛选方法相比,使研究标准化。然而,很难获得用于丑陋鸭子损伤的标有专家标签的图像。因此,我们提议使用自我监督的机器学习自动检测外部损伤。我们首先自动检测和从宽幅皮肤图像中提取所有损伤,并根据自动识别的特征计算在病人图像中嵌入每个已检测到的损伤。这些嵌入器随后被用来计算L2距离,作为衡量差异的一种方法。使用这种深层的学习方法,丑鸭子被确定为外部外科,应该得到检查医生更多的关注。我们通过与皮肤科医生的比较来评估,并在悬置试验中达到72.1%的灵敏度和94.2%的诊断准确度。