Self-paced and self-consistent co-training for semi-supervised image segmentation
This work addresses the challenge of limited labeled data for image segmentation, which is crucial for medical imaging and other domains, though it appears incremental as it builds on existing co-training approaches.
The paper tackles the problem of semi-supervised image segmentation with scarce annotated data by introducing a self-paced and self-consistent co-training method, achieving clear performance advantages over standard baselines and state-of-the-art approaches on three challenging datasets.
Deep co-training has recently been proposed as an effective approach for image segmentation when annotated data is scarce. In this paper, we improve existing approaches for semi-supervised segmentation with a self-paced and self-consistent co-training method. To help distillate information from unlabeled images, we first design a self-paced learning strategy for co-training that lets jointly-trained neural networks focus on easier-to-segment regions first, and then gradually consider harder ones.This is achieved via an end-to-end differentiable loss inthe form of a generalized Jensen Shannon Divergence(JSD). Moreover, to encourage predictions from different networks to be both consistent and confident, we enhance this generalized JSD loss with an uncertainty regularizer based on entropy. The robustness of individual models is further improved using a self-ensembling loss that enforces their prediction to be consistent across different training iterations. We demonstrate the potential of our method on three challenging image segmentation problems with different image modalities, using small fraction of labeled data. Results show clear advantages in terms of performance compared to the standard co-training baselines and recently proposed state-of-the-art approaches for semi-supervised segmentation