Exploring Feature Representation Learning for Semi-supervised Medical Image Segmentation

Abstract

This paper presents a simple yet effective two-stage framework for semi-supervised medical image segmentation. Unlike prior state-of-the-art semi-supervised segmentation methods that predominantly rely on pseudo supervision directly on predictions, such as consistency regularization and pseudo labeling, our key insight is to explore the feature representation learning with labeled and unlabeled (i.e., pseudo labeled) images to regularize a more compact and better-separated feature space, which paves the way for low-density decision boundary learning and therefore enhances the segmentation performance. A stage-adaptive contrastive learning method is proposed, containing a boundary-aware contrastive loss that takes advantage of the labeled images in the first stage, as well as a prototype-aware contrastive loss to optimize both labeled and pseudo labeled images in the second stage. To obtain more accurate prototype estimation, which plays a critical role in prototype-aware contrastive learning, we present an aleatoric uncertainty-aware method, namely AUA, to generate higher-quality pseudo labels. AUA adaptively regularizes prediction consistency by taking advantage of image ambiguity, which, given its significance, is under-explored by existing works. Our method achieves the best results on three public medical image segmentation benchmarks.

Accepted by TNNLS (IEEE Transactions on Neural Networks and Learning Systems). Code available at: https://github.com/Huiimin5/AUA

References in corpus (7)

• Efficient Inference in Fully Connected CRFs with Gaussian Edge Potentials
• Generalised Dice overlap as a deep learning loss function for highly unbalanced segmentations
• Temporal Ensembling for Semi-Supervised Learning
• Shape-aware Semi-supervised 3D Semantic Segmentation for Medical Images
• Uncertainty-aware multi-view co-training for semi-supervised medical image segmentation and domain adaptation
• Semi-Supervised Medical Image Segmentation via Learning Consistency under Transformations
• MutexMatch: Semi-Supervised Learning with Mutex-Based Consistency Regularization