Abstract
Scribble-supervised medical image segmentation tackles the limitation of sparse masks. Conventional approaches alternate between: labeling pseudo-masks and optimizing network parameters. However, such iterative two-stage paradigm is unwieldy and could be trapped in poor local optima since the networks undesirably regress to the erroneous pseudo-masks. To address these issues, we propose a non-iterative method where a stream of varying (pacing) pseudo-masks teach a network via consistency training, named PacingPseudo. Our motivation lies first in a non-iterative process. Interestingly, it can be achieved gracefully by a siamese architecture, wherein a stream of pseudo-masks naturally assimilate a stream of predicted masks during training. Second, we make the consistency training effective with two necessary designs: (i) entropy regularization to obtain high-confidence pseudo-masks for effective teaching; and (ii) distorted augmentations to create discrepancy between the pseudo-mask and predicted-mask streams for consistency regularization. Third, we devise a new memory bank mechanism that provides an extra source of ensemble features to complement scarce labeled pixels. The efficacy of the proposed PacingPseudo is validated on three public medical image datasets, including the segmentation tasks of abdominal multi-organs, cardiac structures, and myocardium. Extensive experiments demonstrate our PacingPseudo improves the baseline by large margins and consistently outcompetes several previous methods. In some cases, our PacingPseudo achieves comparable performance with its fully-supervised counterparts, showing the feasibility of our method for the challenging scribble-supervised segmentation applications. The code and scribble annotations will be publicly available.
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URL
https://arxiv.org/abs/2210.10956
