SUREMap: Predicting Uncertainty in CNN-based Image Reconstruction Using Stein's Unbiased Risk Estimate

• URL: http://arxiv.org/abs/2010.13214v2
• Date: Tue, 20 Apr 2021 03:01:14 GMT
• Title: SUREMap: Predicting Uncertainty in CNN-based Image Reconstruction Using Stein's Unbiased Risk Estimate
• Authors: Ruangrawee Kitichotkul, Christopher A. Metzler, Frank Ong, Gordon Wetzstein
• Abstract summary: Convolutional neural networks (CNN) have emerged as a powerful tool for solving computational imaging reconstruction problems. CNNs are difficult-to-understand black-boxes. This limitation is a major barrier to their use in safety-critical applications like medical imaging.
• Score: 51.67813146731196
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Convolutional neural networks (CNN) have emerged as a powerful tool for solving computational imaging reconstruction problems. However, CNNs are generally difficult-to-understand black-boxes. Accordingly, it is challenging to know when they will work and, more importantly, when they will fail. This limitation is a major barrier to their use in safety-critical applications like medical imaging: Is that blob in the reconstruction an artifact or a tumor? In this work we use Stein's unbiased risk estimate (SURE) to develop per-pixel confidence intervals, in the form of heatmaps, for compressive sensing reconstruction using the approximate message passing (AMP) framework with CNN-based denoisers. These heatmaps tell end-users how much to trust an image formed by a CNN, which could greatly improve the utility of CNNs in various computational imaging applications.

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