While skin cancer detection has been a valuable deep learning application for years, its evaluation has often neglected the context in which testing images are assessed. Traditional melanoma classifiers assume that their testing environments are comparable to the structured images they are trained on. This paper challenges this notion and argues that mole size, a critical attribute in professional dermatology, can be misleading in automated melanoma detection. While malignant melanomas tend to be larger than benign melanomas, relying solely on size can be unreliable and even harmful when contextual scaling of images is not possible. To address this issue, this implementation proposes a custom model that performs various data augmentation procedures to prevent overfitting to incorrect parameters and simulate real-world usage of melanoma detection applications. Multiple custom models employing different forms of data augmentation are implemented to highlight the most significant features of mole classifiers. These implementations emphasize the importance of considering user unpredictability when deploying such applications. The caution required when manually modifying data is acknowledged, as it can result in data loss and biased conclusions. Additionally, the significance of data augmentation in both the dermatology and deep learning communities is considered.

翻译：尽管皮肤癌检测多年来一直是深度学习的重要应用，但其评估往往忽略了测试图像评估的上下文环境。传统的黑色素瘤分类器假设其测试环境与训练所用的结构化图像类似。本文质疑了这一观点，并指出色素痣大小——皮肤科专业诊断中的关键属性——在自动化黑色素瘤检测中可能具有误导性。虽然恶性黑色素瘤通常比良性黑色素瘤更大，但在无法实现图像上下文缩放的情况下，仅凭大小判断可能不可靠，甚至有害。为解决此问题，本研究提出了一种自定义模型，该模型执行多种数据增强过程，以防止对错误参数的过拟合，并模拟黑色素瘤检测应用的真实使用场景。通过采用不同数据增强形式的多个自定义模型，突出了色素痣分类器的最显著特征。这些实现强调了在部署此类应用时考虑用户不可预测性的重要性。本文还认识到手动修改数据时需要谨慎，因为这可能导致数据丢失和偏倚结论。此外，还探讨了数据增强在皮肤科和深度学习领域的重要意义。