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.
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Notes
- 1.
We also conducted experiments with a 3D ResNet18 variant [7] that are out of scope for this publication but are in line with the presented results.
- 2.
Other correlation measures (Pearson and Spearman coefficients) lead to similar conclusions.
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Yilmaz, E.B., Mader, A.O., Fricke, T., Peña, J., Glüer, CC., Meyer, C. (2020). Assessing Attribution Maps for Explaining CNN-Based Vertebral Fracture Classifiers. In: Cardoso, J., et al. Interpretable and Annotation-Efficient Learning for Medical Image Computing. IMIMIC MIL3ID LABELS 2020 2020 2020. Lecture Notes in Computer Science(), vol 12446. Springer, Cham. https://doi.org/10.1007/978-3-030-61166-8_1
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