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Point-based shape decoder: From the embedding vector z, a point representation of N key points is computed using an MLP. The layers each consist of a 1D convolution with the channel size denoted by white font within the blocks, InstanceNorm and ReLU. The number on top of the blocks denotes the size of the dimensionality of the point cloud. Afterwards, a differentiable sampling operation is applied on the key points to obtain a volumetric representation. This step requires N additional parameters y.
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Background: Degenerative spinal pathologies are highly prevalent among the elderly population. Timely diagnosis of osteoporotic fractures and other degenerative deformities enables proactive measures to mitigate the risk of severe back pain and disability. Methods: We explore the use of shape auto-encoders for vertebrae, advancing the state of the...
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Osteoporotic vertebral compression fractures, often resulting from low-energy trauma, markedly impair the quality of life of elderly individuals. The present retrospective study focused on the clinical efficacy of unilateral percutaneous vertebroplasty (PVP) in the treatment of osteoporotic compression fractures. A total of 68 patients, representin...
Citations
... Radi-ologists use the Genant scale (Genant et al., 1993) to measure fracture severity from CT images. Deep Learning can automate VCF detection (Valentinitsch et al., 2019;Tomita et al., 2018;Chettrit et al., 2020;Husseini et al., 2020b;Yilmaz et al., 2021;Engstler et al., 2022;Windsor et al., 2022;Iyer et al., 2023;Hempe et al., 2024), however, only a few works have considered VCF grading, all fully-supervised (Pisov et al., 2020;Zakharov et al., 2023;Wei et al., 2022;Yilmaz et al., 2023). Compared to fracture detection, grading is an even more imbalanced task since medium to severely fractured vertebrae account for only a small portion of overall data. ...
... Compared to fracture detection, grading is an even more imbalanced task since medium to severely fractured vertebrae account for only a small portion of overall data. Closest to our approach, Husseini et al. (2020a) and Hempe et al. (2024) train auto-encoders for vertebra shape reconstruction and then use the learned latent codes for downstream fracture detection. ...
Counterfactual explanations (CEs) aim to enhance the interpretability of machine learning models by illustrating how alterations in input features would affect the resulting predictions. Common CE approaches require an additional model and are typically constrained to binary counterfactuals. In contrast, 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 CEs and the continuous visualization of the model’s internal representation across decision boundaries. Our method leverages the DAE’s ability to encode images into a semantically rich latent space in an unsupervised manner, eliminating the need for labeled data or separate feature extraction models. We show that these latent representations are helpful for medical condition classification and the ordinal regression of severity pathologies, such as vertebral compression fractures (VCF) and diabetic retinopathy (DR). Beyond binary CEs, our method supports the visualization of ordinal CEs using a linear model, providing deeper insights into the model’s decision-making process and enhancing interpretability. Experiments across various medical imaging datasets demonstrate the method’s advantages in interpretability and versatility. The linear manifold of the DAE’s latent space allows for meaningful interpolation and manipulation, making it a powerful tool for exploring medical image properties. Our code is available at https://github.com/matanat/dae_counterfactual</a
... Radi-ologists use the Genant scale (Genant et al., 1993) to measure fracture severity from CT images. Deep Learning can automate VCF detection (Valentinitsch et al., 2019;Tomita et al., 2018;Chettrit et al., 2020;Husseini et al., 2020b;Yilmaz et al., 2021;Engstler et al., 2022;Windsor et al., 2022;Iyer et al., 2023;Hempe et al., 2024), however, only a few works have considered VCF grading, all fully-supervised (Pisov et al., 2020;Zakharov et al., 2023;Wei et al., 2022;Yilmaz et al., 2023). Compared to fracture detection, grading is an even more imbalanced task since medium to severely fractured vertebrae account for only a small portion of overall data. ...
... Compared to fracture detection, grading is an even more imbalanced task since medium to severely fractured vertebrae account for only a small portion of overall data. Closest to our approach, Husseini et al. (2020a) and Hempe et al. (2024) train auto-encoders for vertebra shape reconstruction and then use the learned latent codes for downstream fracture detection. ...
Counterfactual explanations (CEs) aim to enhance the interpretability of machine learning models by illustrating how alterations in input features would affect the resulting predictions. Common CE approaches require an additional model and are typically constrained to binary counterfactuals. In contrast, 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 CEs and the continuous visualization of the model's internal representation across decision boundaries. Our method leverages the DAE's ability to encode images into a semantically rich latent space in an unsupervised manner, eliminating the need for labeled data or separate feature extraction models. We show that these latent representations are helpful for medical condition classification and the ordinal regression of severity pathologies, such as vertebral compression fractures (VCF) and diabetic retinopathy (DR). Beyond binary CEs, our method supports the visualization of ordinal CEs using a linear model, providing deeper insights into the model's decision-making process and enhancing interpretability. Experiments across various medical imaging datasets demonstrate the method's advantages in interpretability and versatility. The linear manifold of the DAE's latent space allows for meaningful interpolation and manipulation, making it a powerful tool for exploring medical image properties. Our code is available at https://github.com/matanat/dae_counterfactual.
