Skin cancer is the most common type of cancer in the United States and is estimated to affect one in five Americans. Recent advances have demonstrated strong performance on skin cancer detection, as exemplified by state of the art performance in the SIIM-ISIC Melanoma Classification Challenge; however these solutions leverage ensembles of complex deep neural architectures requiring immense storage and compute costs, and therefore may not be tractable. A recent movement for TinyML applications is integrating Double-Condensing Attention Condensers (DC-AC) into a self-attention neural network backbone architecture to allow for faster and more efficient computation. This paper explores leveraging an efficient self-attention structure to detect skin cancer in skin lesion images and introduces a deep neural network design with DC-AC customized for skin cancer detection from skin lesion images. The final model is publicly available as a part of a global open-source initiative dedicated to accelerating advancement in machine learning to aid clinicians in the fight against cancer.

通过结合具有高速和更高效计算能力的双重压缩注意力凝聚器（DC-AC）的自注意神经网络骨干架构，本文探讨了利用高效的自注意结构来检测皮肤病变图像中的皮肤癌，并介绍了一种专为皮肤癌检测而定制的深度神经网络设计。该模型作为全球开源倡议的一部分，致力于加速机器学习在医学界对抗癌症方面的发展。

双重浓缩注意力凝聚器：利用深度学习中的注意力来检测皮肤癌