ZooME: Efficient Melanoma Detection Using Zoom-in Attention and Metadata Embedding Deep Neural Network

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.