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Found 10 papers, 5 papers with code
Retrospective Uncertainties for Deep Models using Vine Copulas
Despite the major progress of deep models as learning machines, uncertainty estimation remains a major challenge.
OADAT: Experimental and Synthetic Clinical Optoacoustic Data for Standardized Image Processing
However, no standardized datasets generated with different types of experimental set-up and associated processing methods are available to facilitate advances in broader applications of OA in clinical settings.
Learning Summary Statistics for Bayesian Inference with Autoencoders
For stochastic models with intractable likelihood functions, approximate Bayesian computation offers a way of approximating the true posterior through repeated comparisons of observations with simulated model outputs in terms of a small set of summary statistics.
Probabilistic modeling of lake surface water temperature using a Bayesian spatio-temporal graph convolutional neural network
Nowadays physical models are developed to estimate lake dynamics; however, computations needed for accurate estimation of lake surface temperature can get prohibitively expensive.
Delineating Bone Surfaces in B-Mode Images Constrained by Physics of Ultrasound Propagation
Bone surface delineation in ultrasound is of interest due to its potential in diagnosis, surgical planning, and post-operative follow-up in orthopedics, as well as the potential of using bones as anatomical landmarks in surgical navigation.
Active Learning for Segmentation Based on Bayesian Sample Queries
In an active learning framework of selecting informed samples for manual labeling, expert clinician time for manual annotation can be optimally utilized, enabling the establishment of large labeled datasets for machine learning.
Extending Pretrained Segmentation Networks with Additional Anatomical Structures
We propose a class-incremental segmentation framework for extending a deep network trained for some anatomical structure to yet another structure using a small incremental annotation set.
Generative Adversarial Networks for MR-CT Deformable Image Registration
Performance for the abdominal region was similar to that of CT-MRI NMI registration (77. 4 vs. 78. 8%) when using 3D synthesizing MRIs (12 slices) and medium sized receptive fields for the discriminator.
Active Learning for Segmentation by Optimizing Content Information for Maximal Entropy
Segmentation is essential for medical image analysis tasks such as intervention planning, therapy guidance, diagnosis, treatment decisions.
Learn the new, keep the old: Extending pretrained models with new anatomy and images
Deep learning has been widely accepted as a promising solution for medical image segmentation, given a sufficiently large representative dataset of images with corresponding annotations.
