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Found 5 papers, 3 papers with code
Investigating and Improving Latent Density Segmentation Models for Aleatoric Uncertainty Quantification in Medical Imaging
The results indicate that by encouraging a homogeneous latent space, one can significantly improve latent density modeling for medical image segmentation.
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.
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