Signature Activation: A Sparse Signal View for Holistic Saliency
Jose Roberto Tello Ayala Akl C. Fahed Weiwei Pan Eugene V. Pomerantsev Patrick T. Ellinor Anthony Philippakis Finale Doshi-Velez
The adoption of machine learning in healthcare calls for model transparency
and explainability. In this work, we introduce Signature Activation, a saliency
method that generates holistic and class-agnostic explanations for
Convolutional Neural Network (CNN) outputs. Our method exploits the fact that
certain kinds of medical images, such as angiograms, have clear foreground and
background objects. We give theoretical explanation to justify our methods. We
show the potential use of our method in clinical settings through evaluating
its efficacy for aiding the detection of lesions in coronary angiograms.