Signature Activation: A Sparse Signal View for Holistic Saliency
Abstract
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 Networks' outputs. We exploit the sparsity of images and 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 Angiorams.
Cite
Text
Ayala et al. "Signature Activation: A Sparse Signal View for Holistic Saliency." ICML 2023 Workshops: IMLH, 2023.Markdown
[Ayala et al. "Signature Activation: A Sparse Signal View for Holistic Saliency." ICML 2023 Workshops: IMLH, 2023.](https://mlanthology.org/icmlw/2023/ayala2023icmlw-signature/)BibTeX
@inproceedings{ayala2023icmlw-signature,
title = {{Signature Activation: A Sparse Signal View for Holistic Saliency}},
author = {Ayala, Jose Roberto Tello and Fahed, Akl C. and Pan, Weiwei and Pomerantsev, Eugene V. and Ellinor, Patrick Thomas and Philippakis, Anthony and Doshi-Velez, Finale},
booktitle = {ICML 2023 Workshops: IMLH},
year = {2023},
url = {https://mlanthology.org/icmlw/2023/ayala2023icmlw-signature/}
}