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Meta Corrupted Pixels Mining for Medical Image Segmentation
[article]
2020
arXiv
pre-print
Deep neural networks have achieved satisfactory performance in piles of medical image analysis tasks. However the training of deep neural network requires a large amount of samples with high-quality annotations. In medical image segmentation, it is very laborious and expensive to acquire precise pixel-level annotations. Aiming at training deep segmentation models on datasets with probably corrupted annotations, we propose a novel Meta Corrupted Pixels Mining (MCPM) method based on a simple meta
arXiv:2007.03538v1
fatcat:caioptfd6vd4npmubuuah33gli