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Found 6 papers, 3 papers with code
Enabling Uncertainty Estimation in Iterative Neural Networks
Turning pass-through network architectures into iterative ones, which use their own output as input, is a well-known approach for boosting performance.
Enforcing connectivity of 3D linear structures using their 2D projections
Many biological and medical tasks require the delineation of 3D curvilinear structures such as blood vessels and neurites from image volumes.
Adjusting the Ground Truth Annotations for Connectivity-Based Learning to Delineate
Deep learning-based approaches to delineating 3D structure depend on accurate annotations to train the networks.
Persistent Homology with Improved Locality Information for more Effective Delineation
Persistent Homology (PH) has been successfully used to train networks to detect curvilinear structures and to improve the topological quality of their results.
Localized Persistent Homologies for more Effective Deep Learning
Persistent Homologies have been successfully used to increase the performance of deep networks trained to detect curvilinear structures and to improve the topological quality of the results.
Promoting Connectivity of Network-Like Structures by Enforcing Region Separation
The main idea behind our loss is to express the connectivity of roads, or canals, in terms of disconnections that they create between background regions of the image.
