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Found 12 papers, 9 papers with code
Automated Thoracolumbar Stump Rib Detection and Analysis in a Large CT Cohort
Thoracolumbar stump ribs are one of the essential indicators of thoracolumbar transitional vertebrae or enumeration anomalies.
MAGO-SP: Detection and Correction of Water-Fat Swaps in Magnitude-Only VIBE MRI
While the two-point VIBE provides water-fat-separated images, the six-point VIBE allows estimation of the effective transversal relaxation rate R2* and the proton density fat fraction (PDFF), which are imaging markers for health and disease.
PARASIDE: An Automatic Paranasal Sinus Segmentation and Structure Analysis Tool for MRI
While the soft tissue segmentation is good, the automated annotations of the air volumes are excellent.
Detecting Unforeseen Data Properties with Diffusion Autoencoder Embeddings using Spine MRI data
Our findings highlight the potential of using advanced embedding techniques like DAEs to detect data quality issues and biases in medical imaging datasets.
Neural Network Surrogate and Projected Gradient Descent for Fast and Reliable Finite Element Model Calibration: a Case Study on an Intervertebral Disc
The proposed method is evaluated against SOTA Genetic Algorithm and inverse model baselines on synthetic and in vitro experimental datasets.
Counterfactual Explanations for Medical Image Classification and Regression using Diffusion Autoencoder
Counterfactual explanations (CEs) aim to enhance the interpretability of machine learning models by illustrating how alterations in input features would affect the resulting predictions.
TotalVibeSegmentator: Full Body MRI Segmentation for the NAKO and UK Biobank
Objectives: To present a publicly available torso segmentation network for large epidemiology datasets on volumetric interpolated breath-hold examination (VIBE) images.
SPINEPS -- Automatic Whole Spine Segmentation of T2-weighted MR images using a Two-Phase Approach to Multi-class Semantic and Instance Segmentation
Training on auto-generated annotations and evaluating on manually corrected test data from the GNC yielded global dice scores of 0. 900 for vertebrae, 0. 960 for intervertebral discs, and 0. 947 for the spinal canal.
Density-based Feasibility Learning with Normalizing Flows for Introspective Robotic Assembly
Machine Learning (ML) models in Robotic Assembly Sequence Planning (RASP) need to be introspective on the predicted solutions, i. e. whether they are feasible or not, to circumvent potential efficiency degradation.
Semantic Latent Space Regression of Diffusion Autoencoders for Vertebral Fracture Grading
We then regress the severity of the fracture as a function of the distance to this hyperplane, calibrating the results to the Genant scale.
Efficient and Feasible Robotic Assembly Sequence Planning via Graph Representation Learning
With GRACE, we are able to extract meaningful information from the graph input and predict assembly sequences in a step-by-step manner.
CheXplaining in Style: Counterfactual Explanations for Chest X-rays using StyleGAN
Deep learning models used in medical image analysis are prone to raising reliability concerns due to their black-box nature.
