mlsanityData to 7 Oct 2026

Interpretable Vertebral Fracture Diagnosis

arXiv 2203.16273 ↗First posted 30 Mar 2022eess.IV cs.CVcited by 4

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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.

Key facts. Cited by 4 papers in the map (data through 7 Oct 2026). Builds on 19 of its 40 references that are in the map. First posted on arXiv 30 Mar 2022.

Builds on

19 of 40 references.

References · 19

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7 more
  1. 2021Towards Semantic Interpretation of Thoracic Disease and COVID-19 Diagnosis Modelscited by 9
  2. 2020Keypoints Localization for Joint Vertebra Detection and Fracture Severity Quantificationcited by 8
  3. 2020Grading Loss: A Fracture Grade-based Metric Loss for Vertebral Fracture Detectioncited by 8
  4. 2020Rethinking Positive Aggregation and Propagation of Gradients in Gradient-based Saliency Methodscited by 7
  5. 2017Compression Fractures Detection on CTcited by 5
  6. 20203D Convolutional Sequence to Sequence Model for Vertebral Compression Fractures Identification in CTcited by 5
  7. 2020Two-Stream Compare and Contrast Network for Vertebral Compression Fracture Diagnosiscited by 3

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Cited by year

Citing papers per year: 2023: 2; 2024: 2.

4 papers.

  1. 2024Counterfactual Explanations for Medical Image Classification and Regression using Diffusion Autoencodercited by 10
  2. 2023Pixel-Level Explanation of Multiple Instance Learning Models in Biomedical Single Cell Imagescited by 9
  3. 2023Semantic Latent Space Regression of Diffusion Autoencoders for Vertebral Fracture Gradingcited by 2
  4. 2024Enhancing Interpretability of Vertebrae Fracture Grading using Human-interpretable Prototypescited by 0