Assessing the validity of saliency maps for abnormality localization in medical imaging

• URL: http://arxiv.org/abs/2006.00063v1
• Date: Fri, 29 May 2020 20:17:26 GMT
• Title: Assessing the validity of saliency maps for abnormality localization in medical imaging
• Authors: Nishanth Thumbavanam Arun, Nathan Gaw, Praveer Singh, Ken Chang, Katharina Viktoria Hoebel, Jay Patel, Mishka Gidwani, Jayashree Kalpathy-Cramer
• Abstract summary: Saliency maps have become a widely used method to assess which areas of the input image are most pertinent to the prediction of a trained neural network. In this work, we explored the credibility of the various existing saliency map methods on the RSNA Pneumonia dataset.
• Score: 6.299152745637685
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Saliency maps have become a widely used method to assess which areas of the input image are most pertinent to the prediction of a trained neural network. However, in the context of medical imaging, there is no study to our knowledge that has examined the efficacy of these techniques and quantified them using overlap with ground truth bounding boxes. In this work, we explored the credibility of the various existing saliency map methods on the RSNA Pneumonia dataset. We found that GradCAM was the most sensitive to model parameter and label randomization, and was highly agnostic to model architecture.

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