Self-paced and self-consistent co-training for semi-supervised image segmentation

TL;DR

This paper introduces a self-paced, self-consistent co-training method for semi-supervised image segmentation, leveraging uncertainty regularization and self-ensembling to improve performance with limited labeled data.

Contribution

It proposes a novel self-paced and self-consistent co-training framework with an uncertainty regularizer and self-ensembling, enhancing semi-supervised segmentation performance.

Findings

Outperforms standard co-training baselines.

Achieves state-of-the-art results on multiple datasets.

Effective with limited labeled data.

Abstract

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