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Found 6 papers, 4 papers with code
Advancing 6-DoF Instrument Pose Estimation in Variable X-Ray Imaging Geometries
We propose a general-purpose approach of data acquisition for 6-DoF pose estimation tasks in X-ray systems, a novel and general purpose YOLOv5-6D pose architecture for accurate and fast object pose estimation and a complete method for surgical screw pose estimation under acquisition geometry consideration from a monocular cone-beam X-ray image.
Investigating and Improving Latent Density Segmentation Models for Aleatoric Uncertainty Quantification in Medical Imaging
Data uncertainties, such as sensor noise, occlusions or limitations in the acquisition method can introduce irreducible ambiguities in images, which result in varying, yet plausible, semantic hypotheses.
Probabilistic 3D segmentation for aleatoric uncertainty quantification in full 3D medical data
To this end, we have developed a 3D probabilistic segmentation framework augmented with NFs, to enable capturing the distributions of various complexity.
Towards real-time 6D pose estimation of objects in single-view cone-beam X-ray
Deep learning-based pose estimation algorithms can successfully estimate the pose of objects in an image, especially in the field of color images.
Efficient Out-of-Distribution Detection of Melanoma with Wavelet-based Normalizing Flows
In this work, we aim at using these biases with domain-level knowledge of melanoma, to improve likelihood-based OOD detection of malignant images.
Improved Pancreatic Tumor Detection by Utilizing Clinically-Relevant Secondary Features
Pancreatic cancer is one of the global leading causes of cancer-related deaths.
