Assessing Attribution Maps for Explaining CNN-Based Vertebral Fracture Classifiers

Abstract

Automated evaluation of vertebral fracture status on computed tomography (CT) scans acquired for various purposes (opportunistic CT) may substantially enhance vertebral fracture detection rate. Convolutional neural networks (CNNs) have shown promising performance in numerous tasks but their black box nature may hinder acceptance by physicians. We aim (a) to evaluate CNN architectures for osteoporotic fracture discrimination as part of a pipeline localizing and classifying vertebrae in CT images and (b) to evaluate the benefit of using attribution maps to explain a network’s decision. Training different model architectures on 3D patches containing vertebrae, we show that CNNs permit highly accurate discrimination of the fracture status of individual vertebrae. Explanations were computed using selected attribution methods: Gradient, Gradient * Input, Guided BackProp, and SmoothGrad algorithms. Quantitative and visual tests were conducted to evaluate the meaningfulness of the explanations (sanity checks). The explanations were found to depend on the model architecture, the realization of the parameters, and the precise position of the target object of interest.