Search Results for author:
Found 2 papers, 0 papers with code
Distilling effective supervision for robust medical image segmentation with noisy labels
In particular, we explicitly estimate the uncertainty of every pixel as pixel-wise noise estimation, and propose pixel-wise robust learning by using both the original labels and pseudo labels.
Pick-and-Learn: Automatic Quality Evaluation for Noisy-Labeled Image Segmentation
We propose a solution for network automatically evaluating the relative quality of the labels in the training set and using good ones to tune the network parameters.
Cannot find the paper you are looking for? You can
Submit a new open access paper.
