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Found 3 papers, 1 papers with code
A Positive/Unlabeled Approach for the Segmentation of Medical Sequences using Point-Wise Supervision
While most methods of this kind assume that the proportion of positive samples in the data is known a-priori, we introduce a novel self-supervised method to estimate this prior efficiently by combining a Bayesian estimation framework and new stopping criteria.
Iterative multi-path tracking for video and volume segmentation with sparse point supervision
Our object model is then used in a graph-based optimization problem that takes into account all provided locations and the image data in order to infer the complete pixel-wise segmentation.
Expected exponential loss for gaze-based video and volume ground truth annotation
Many recent machine learning approaches used in medical imaging are highly reliant on large amounts of image and ground truth data.
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