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Found 4 papers, 2 papers with code
Leveraging point annotations in segmentation learning with boundary loss
Hence it is intuitively inappropriate for weak supervision, where the ground truth label may be much smaller than the actual object and a certain amount of false positives (w. r. t.
Fast Graph-Cut Based Optimization for Practical Dense Deformable Registration of Volume Images
Objective: Deformable image registration is a fundamental problem in medical image analysis, with applications such as longitudinal studies, population modeling, and atlas based image segmentation.
When can $l_p$-norm objective functions be minimized via graph cuts?
These techniques can be used to minimize objective functions written as the sum of a set of unary and pairwise terms, provided that the objective function is submodular.
PDNet: Semantic Segmentation integrated with a Primal-Dual Network for Document binarization
Binarization of digital documents is the task of classifying each pixel in an image of the document as belonging to the background (parchment/paper) or foreground (text/ink).
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