# Interpretable Vertebral Fracture Diagnosis

arXiv: https://arxiv.org/abs/2203.16273 · HTML: https://mlsanity.com/paper/2203.16273/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 19 of its 40 references that are in the map.
- First posted on arXiv 30 Mar 2022.

## Builds on (most cited first, 19 of 19)

- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [A Unified Approach to Interpreting Model Predictions](https://mlsanity.com/paper/1705.07874/) (2017)
- [Learning Deep Features for Discriminative Localization](https://mlsanity.com/paper/1512.04150/) (2015)
- [3D U-Net: Learning Dense Volumetric Segmentation from Sparse Annotation](https://mlsanity.com/paper/1606.06650/) (2016)
- [Network Dissection: Quantifying Interpretability of Deep Visual Representations](https://mlsanity.com/paper/1704.05796/) (2017)
- [VerSe: A Vertebrae Labelling and Segmentation Benchmark for Multi-detector CT Images](https://mlsanity.com/paper/2001.09193/) (2020)
- [Restricting the Flow: Information Bottlenecks for Attribution](https://mlsanity.com/paper/2001.00396/) (2020)
- [Do Explanations Explain? Model Knows Best](https://mlsanity.com/paper/2203.02269/) (2022)
- [Fine-Grained Neural Network Explanation by Identifying Input Features with Predictive Information](https://mlsanity.com/paper/2110.01471/) (2021)
- [DeepMiner: Discovering Interpretable Representations for Mammogram Classification and Explanation](https://mlsanity.com/paper/1805.12323/) (2018)
- [Detection of vertebral fractures in CT using 3D Convolutional Neural Networks](https://mlsanity.com/paper/1911.01816/) (2019)
- [Explaining COVID-19 and Thoracic Pathology Model Predictions by Identifying Informative Input Features](https://mlsanity.com/paper/2104.00411/) (2021)
- [Towards Semantic Interpretation of Thoracic Disease and COVID-19 Diagnosis Models](https://mlsanity.com/paper/2104.02481/) (2021)
- [Keypoints Localization for Joint Vertebra Detection and Fracture Severity Quantification](https://mlsanity.com/paper/2005.11960/) (2020)
- [Grading Loss: A Fracture Grade-based Metric Loss for Vertebral Fracture Detection](https://mlsanity.com/paper/2008.07831/) (2020)
- [Rethinking Positive Aggregation and Propagation of Gradients in Gradient-based Saliency Methods](https://mlsanity.com/paper/2012.00362/) (2020)
- [Compression Fractures Detection on CT](https://mlsanity.com/paper/1706.01671/) (2017)
- [3D Convolutional Sequence to Sequence Model for Vertebral Compression Fractures Identification in CT](https://mlsanity.com/paper/2010.03739/) (2020)
- [Two-Stream Compare and Contrast Network for Vertebral Compression Fracture Diagnosis](https://mlsanity.com/paper/2010.06224/) (2020)

## Built on by (most cited first, 4 of 4)

- [Counterfactual Explanations for Medical Image Classification and Regression using Diffusion Autoencoder](https://mlsanity.com/paper/2408.01571/) (2024)
- [Pixel-Level Explanation of Multiple Instance Learning Models in Biomedical Single Cell Images](https://mlsanity.com/paper/2303.08632/) (2023)
- [Semantic Latent Space Regression of Diffusion Autoencoders for Vertebral Fracture Grading](https://mlsanity.com/paper/2303.12031/) (2023)
- [Enhancing Interpretability of Vertebrae Fracture Grading using Human-interpretable Prototypes](https://mlsanity.com/paper/2404.02830/) (2024)

## Abstract (arXiv)

Do black-box neural network models learn clinically relevant features for fracture diagnosis? The answer not only establishes reliability quenches scientific curiosity but also leads to explainable and verbose findings that can assist the radiologists in the final and increase trust. This work identifies the concepts networks use for vertebral fracture diagnosis in CT images. This is achieved by associating concepts to neurons highly correlated with a specific diagnosis in the dataset. The concepts are either associated with neurons by radiologists pre-hoc or are visualized during a specific prediction and left for the user's interpretation. We evaluate which concepts lead to correct diagnosis and which concepts lead to false positives. The proposed frameworks and analysis pave the way for reliable and explainable vertebral fracture diagnosis.