Deep learning implemented with convolutional network architectures can exceed specialists' diagnostic accuracy. However, whole-image deep learning trained on a given dataset may not generalize to other datasets. The problem arises because extra-lesional features - ruler marks, ink marks, and other melanoma correlates - may serve as information leaks. These extra-lesional features, discoverable by heat maps, degrade melanoma diagnostic performance and cause techniques learned on one data set to fail to generalize. We propose a novel technique to improve melanoma recognition by an EfficientNet model. The model trains the network to detect the lesion and learn features from the detected lesion. A generalizable elliptical segmentation model for lesions was developed, with an ellipse enclosing a lesion and the ellipse enclosed by an extended rectangle (bounding box). The minimal bounding box was extended by 20% to allow some background around the lesion. The publicly available International Skin Imaging Collaboration (ISIC) 2020 skin lesion image dataset was used to evaluate the effectiveness of the proposed method. Our test results show that the proposed method improved diagnostic accuracy by increasing the mean area under receiver operating characteristic curve (mean AUC) score from 0.9 to 0.922. Additionally, correctly diagnosed scores are also improved, providing better separation of scores, thereby increasing melanoma diagnostic confidence. The proposed lesion-focused convolutional technique warrants further study.

翻译：采用卷积网络架构的深度学习在诊断准确率上可超越专家水平。然而，基于特定数据集训练的整图深度学习可能无法泛化至其他数据集。产生该问题的原因在于病灶外特征——刻度标记、墨水标记及其他黑色素瘤相关标志——可能形成信息泄露。这些可通过热力图发现的病灶外特征会降低黑色素瘤诊断性能，并导致在某数据集上习得的技术无法泛化。我们提出一种新型技术，通过EfficientNet模型提升黑色素瘤识别能力。该模型训练网络检测病灶并从检测到的病灶中学习特征。我们开发了一种具有泛化能力的椭圆形病灶分割模型，其中椭圆包围病灶，而椭圆被扩展矩形（边界框）所包围。最小边界框被扩展20%以保留病灶周围的部分背景。采用公开的国际皮肤影像协作组织（ISIC）2020皮肤病变图像数据集评估所提方法的有效性。测试结果表明，所提方法将受试者工作特征曲线下平均面积（mean AUC）从0.9提升至0.922，从而提高了诊断准确率。此外，正确诊断得分亦得到改善，得分分布分离度更优，从而增强了黑色素瘤诊断置信度。所提出的病灶聚焦卷积技术值得进一步研究。