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Weakly Supervised Volumetric Segmentation via Self-taught Shape Denoising Model
[article]
2021
arXiv
pre-print
Weakly supervised segmentation is an important problem in medical image analysis due to the high cost of pixelwise annotation. Prior methods, while often focusing on weak labels of 2D images, exploit few structural cues of volumetric medical images. To address this, we propose a novel weakly-supervised segmentation strategy capable of better capturing 3D shape prior in both model prediction and learning. Our main idea is to extract a self-taught shape representation by leveraging weak labels,
arXiv:2104.13082v2
fatcat:gbkdduumrraldglhprpow7hp7q