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Skin cancer is the most common type of cancer in the United States and isestimated to affect one in five Americans. Recent advances have demonstratedstrong performance on skin cancer detection, as exemplified by state of the artperformance in the SIIM-ISIC Melanoma Classification Challenge; however thesesolutions leverage ensembles of complex deep neural architectures requiringimmense storage and compute costs, and therefore may not be tractable. A recentmovement for TinyML applications is integrating Double-Condensing AttentionCondensers (DC-AC) into a self-attention neural network backbone architectureto allow for faster and more efficient computation. This paper exploresleveraging an efficient self-attention structure to detect skin cancer in skinlesion images and introduces a deep neural network design with DC-AC customizedfor skin cancer detection from skin lesion images. The final model is publiclyavailable as a part of a global open-source initiative dedicated toaccelerating advancement in machine learning to aid clinicians in the fightagainst cancer.

Double-Condensing Attention Condenser: Leveraging Attention in Deep Learning to Detect Skin Cancer from Skin Lesion Images