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.

翻译：皮肤癌是美国最常见的癌症类型，预计每五个美国人中就可能有一人患病。近期研究在皮肤癌检测方面展现出强大性能，这在SIIM-ISIC黑色素瘤分类挑战赛的最优表现中得到印证；然而，这些解决方案依赖由复杂深度神经网络架构组成的集成模型，需要巨大的存储和计算成本，因此可能难以实际应用。最新TinyML应用趋势是将双聚缩注意力冷凝器（DC-AC）集成到自注意力神经网络骨干架构中，以实现更快速高效的计算。本文探索利用高效自注意力结构从皮肤病变图像中检测皮肤癌，并引入一种专为基于皮肤病变图像的皮肤癌检测定制的集成DC-AC深度神经网络设计。最终模型作为全球开源倡议的一部分公开发布，该倡议致力于加速机器学习发展以协助临床医生抗击癌症。