U-Noise: Learnable Noise Masks for Interpretable Image Segmentation
- URL: http://arxiv.org/abs/2101.05791v2
- Date: Wed, 20 Jan 2021 17:04:28 GMT
- Title: U-Noise: Learnable Noise Masks for Interpretable Image Segmentation
- Authors: Teddy Koker, Fatemehsadat Mireshghallah, Tom Titcombe, Georgios
Kaissis
- Abstract summary: We introduce a new method for interpreting image segmentation models by learning regions of images in which noise can be applied without hindering downstream model performance.
We show that, unlike other methods, our interpretability model can be quantitatively evaluated based on the downstream performance over obscured images.
- Score: 0.7646713951724011
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: Deep Neural Networks (DNNs) are widely used for decision making in a myriad
of critical applications, ranging from medical to societal and even judicial.
Given the importance of these decisions, it is crucial for us to be able to
interpret these models. We introduce a new method for interpreting image
segmentation models by learning regions of images in which noise can be applied
without hindering downstream model performance. We apply this method to
segmentation of the pancreas in CT scans, and qualitatively compare the quality
of the method to existing explainability techniques, such as Grad-CAM and
occlusion sensitivity. Additionally we show that, unlike other methods, our
interpretability model can be quantitatively evaluated based on the downstream
performance over obscured images.
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