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Signature Activation: A Sparse Signal View for Holistic Saliency

arXiv 2309.11443 ↗First posted 20 Sep 2023cs.CV cs.LGcited by 0

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Abstract (arXiv)

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.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 18 of its 37 references that are in the map. First posted on arXiv 20 Sep 2023.

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18 of 37 references.

References · 18

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6 more
  1. 2018Deep Learning for Generic Object Detection: A Surveycited by 392
  2. 2017Methods for Interpreting and Understanding Deep Neural Networkscited by 345
  3. 2020Eigen-CAM: Class Activation Map using Principal Componentscited by 189
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  5. 2016Convolutional Neural Networks Analyzed via Convolutional Sparse Codingcited by 52
  6. 2017CNN Fixations: An unraveling approach to visualize the discriminative image regionscited by 9

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