Double-Condensing Attention Condenser: Leveraging Attention in Deep
  Learning to Detect Skin Cancer from Skin Lesion Images
Chi-en Amy Tai Elizabeth Janes Chris Czarnecki Alexander Wong
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.