Semantic Latent Space Regression of Diffusion Autoencoders for Vertebral Fracture Grading

• URL: http://arxiv.org/abs/2303.12031v1
• Date: Tue, 21 Mar 2023 17:16:01 GMT
• Title: Semantic Latent Space Regression of Diffusion Autoencoders for Vertebral Fracture Grading
• Authors: Matthias Keicher, Matan Atad, David Schinz, Alexandra S. Gersing, Sarah C. Foreman, Sophia S. Goller, Juergen Weissinger, Jon Rischewski, Anna-Sophia Dietrich, Benedikt Wiestler, Jan S. Kirschke, Nassir Navab
• Abstract summary: This paper proposes a novel approach to train a generative Diffusion Autoencoder model as an unsupervised feature extractor. We model fracture grading as a continuous regression, which is more reflective of the smooth progression of fractures. Importantly, the generative nature of our method allows us to visualize different grades of a given vertebra, providing interpretability and insight into the features that contribute to automated grading.
• Score: 72.45699658852304
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Vertebral fractures are a consequence of osteoporosis, with significant health implications for affected patients. Unfortunately, grading their severity using CT exams is hard and subjective, motivating automated grading methods. However, current approaches are hindered by imbalance and scarcity of data and a lack of interpretability. To address these challenges, this paper proposes a novel approach that leverages unlabelled data to train a generative Diffusion Autoencoder (DAE) model as an unsupervised feature extractor. We model fracture grading as a continuous regression, which is more reflective of the smooth progression of fractures. Specifically, we use a binary, supervised fracture classifier to construct a hyperplane in the DAE's latent space. We then regress the severity of the fracture as a function of the distance to this hyperplane, calibrating the results to the Genant scale. Importantly, the generative nature of our method allows us to visualize different grades of a given vertebra, providing interpretability and insight into the features that contribute to automated grading.

Related papers

• Counterfactual Explanations for Medical Image Classification and Regression using Diffusion Autoencoder [38.81441978142279]
  We propose a novel method that operates directly on the latent space of a generative model, specifically a Diffusion Autoencoder (DAE) This approach offers inherent interpretability by enabling the generation of Counterfactual explanations (CEs) We show that these latent representations are helpful for medical condition classification and the ordinal regression of pathologies, such as vertebral compression fractures (VCF) and diabetic retinopathy (DR)
  arXiv  Detail & Related papers  (2024-08-02T21:01:30Z)
• Enhancing Interpretability of Vertebrae Fracture Grading using Human-interpretable Prototypes [7.633493982907541]
  We propose a novel interpretable-by-design method, ProtoVerse, to find relevant sub-parts of vertebral fractures (prototypes) that reliably explain the model's decision in a human-understandable way. We have experimented with the VerSe'19 dataset and outperformed the existing prototype-based method.
  arXiv  Detail & Related papers  (2024-04-03T16:04:59Z)
• Shape Matters: Detecting Vertebral Fractures Using Differentiable Point-Based Shape Decoding [51.38395069380457]
  Degenerative spinal pathologies are highly prevalent among the elderly population. Timely diagnosis of osteoporotic fractures and other degenerative deformities facilitates proactive measures to mitigate the risk of severe back pain and disability. In this study, we specifically explore the use of shape auto-encoders for vertebrae.
  arXiv  Detail & Related papers  (2023-12-08T18:11:22Z)
• Automatic diagnosis of knee osteoarthritis severity using Swin transformer [55.01037422579516]
  Knee osteoarthritis (KOA) is a widespread condition that can cause chronic pain and stiffness in the knee joint. We propose an automated approach that employs the Swin Transformer to predict the severity of KOA.
  arXiv  Detail & Related papers  (2023-07-10T09:49:30Z)
• Fuzzy Attention Neural Network to Tackle Discontinuity in Airway Segmentation [67.19443246236048]
  Airway segmentation is crucial for the examination, diagnosis, and prognosis of lung diseases. Some small-sized airway branches (e.g., bronchus and terminaloles) significantly aggravate the difficulty of automatic segmentation. This paper presents an efficient method for airway segmentation, comprising a novel fuzzy attention neural network and a comprehensive loss function.
  arXiv  Detail & Related papers  (2022-09-05T16:38:13Z)
• An Interpretable Approach to Automated Severity Scoring in Pelvic Trauma [3.762671763237911]
  Pelvic ring disruptions result from blunt injury mechanisms and are often found in patients with multi-system trauma. To grade pelvic fracture severity in trauma victims based on whole-body CT, the Tile AO/OTA classification is frequently used. This paper introduces an automated yet interpretable pelvic trauma decision support system to assist radiologists in fracture detection and Tile grade classification.
  arXiv  Detail & Related papers  (2021-05-21T09:52:33Z)
• Grading Loss: A Fracture Grade-based Metric Loss for Vertebral Fracture Detection [58.984536305767996]
  We propose a representation learning-inspired approach for automated vertebral fracture detection. We present a novel Grading Loss for learning representations that respect Genant's fracture grading scheme. On a publicly available spine dataset, the proposed loss function achieves a fracture detection F1 score of 81.5%.
  arXiv  Detail & Related papers  (2020-08-18T10:03:45Z)
• Improved Slice-wise Tumour Detection in Brain MRIs by Computing Dissimilarities between Latent Representations [68.8204255655161]
  Anomaly detection for Magnetic Resonance Images (MRIs) can be solved with unsupervised methods. We have proposed a slice-wise semi-supervised method for tumour detection based on the computation of a dissimilarity function in the latent space of a Variational AutoEncoder. We show that by training the models on higher resolution images and by improving the quality of the reconstructions, we obtain results which are comparable with different baselines.
  arXiv  Detail & Related papers  (2020-07-24T14:02:09Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.