Annu Int Conf IEEE Eng Med Biol Soc. 2021 Nov;2021:4041-4044. doi: 10.1109/EMBC46164.2021.9630452.
Melanoma detection is a crucial yet hard task for both dermatologists and computer-aided diagnosis (CAD). Many traditional machine learning algorithms including deep learning-based methods are employed for melanoma classification. However, more and more complex network architectures do not harvest a leap in model performance. In this paper, we aim to enhance the credibility of CAD approach for melanoma by paying more attention to clinically important information. We propose a Zoom-in Attention and Metadata Embedding (ZooME) melanoma detection network by: 1) introducing a Zoom-in Attention model to better extract and utilize unique pathological information of dermoscopy images; 2) embedding patients' demographic information including age, gender, and anatomic body site, to provide well-rounded information for better prediction. We apply a ten-fold cross-validation on the latest ISIC-2020 dataset with 33,126 dermoscopy images. The proposed ZooME achieved state-of-the-art results with 92.23% in AUC score, 84.59% in accuracy, 85.95% in sensitivity, and 84.63% in specialty, respectively.
黑色素瘤检测对皮肤科医生和计算机辅助诊断（CAD）来说都是一项至关重要但具有挑战性的任务。许多传统的机器学习算法，包括基于深度学习的方法，都被用于黑色素瘤分类。然而，越来越复杂的网络架构并没有带来模型性能的显著提升。在本文中，我们旨在通过更加关注临床重要信息，提高 CAD 方法在黑色素瘤检测中的可信度。我们提出了一种 Zoom-in Attention 和 Metadata Embedding（ZooME）黑色素瘤检测网络，方法是：1）引入 Zoom-in Attention 模型，以更好地提取和利用皮肤镜图像的独特病理信息；2）嵌入患者的人口统计学信息，包括年龄、性别和解剖部位，为更好的预测提供全面的信息。我们在最新的 ISIC-2020 数据集上进行了十折交叉验证，该数据集包含 33126 张皮肤镜图像。所提出的 ZooME 在 AUC 评分、准确率、敏感度和特异性方面分别达到了 92.23%、84.59%、85.95%和 84.63%的最先进水平。
