Quantifying Statistical Significance of Neural Network-based Image Segmentation by Selective Inference
Keywords: Image Segmentation, Uncertainty Quantification, Selective Inference, Statistical Hypothesis Testing, p-value
TL;DR: We propose a novel method to quantify the reliability of neural network-based image segmentation in statistical hypothesis testing framework by Selective Inference.
Abstract: Although a vast body of literature relates to image segmentation methods that use deep neural networks (DNNs), less attention has been paid to assessing the statistical reliability of segmentation results. In this study, we interpret the segmentation results as hypotheses driven by DNN (called DNN-driven hypotheses) and propose a method to quantify the reliability of these hypotheses within a statistical hypothesis testing framework. To this end, we introduce a conditional selective inference (SI) framework---a new statistical inference framework for data-driven hypotheses that has recently received considerable attention---to compute exact (non-asymptotic) valid p-values for the segmentation results. To use the conditional SI framework for DNN-based segmentation, we develop a new SI algorithm based on the homotopy method, which enables us to derive the exact (non-asymptotic) sampling distribution of DNN-driven hypothesis. We conduct several experiments to demonstrate the performance of the proposed method.
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