  The volume-wise labeling of 3D medical images is expertise-demanded and
time-consuming; hence semi-supervised learning (SSL) is highly desirable for
training with limited labeled data. Imbalanced class distribution is a severe
problem that bottlenecks the real-world application of these methods but was
not addressed much. Aiming to solve this issue, we present a novel
Dual-debiased Heterogeneous Co-training (DHC) framework for semi-supervised 3D
medical image segmentation. Specifically, we propose two loss weighting
strategies, namely Distribution-aware Debiased Weighting (DistDW) and
Difficulty-aware Debiased Weighting (DiffDW), which leverage the pseudo labels
dynamically to guide the model to solve data and learning biases. The framework
improves significantly by co-training these two diverse and accurate
sub-models. We also introduce more representative benchmarks for
class-imbalanced semi-supervised medical image segmentation, which can fully
demonstrate the efficacy of the class-imbalance designs. Experiments show that
our proposed framework brings significant improvements by using pseudo labels
for debiasing and alleviating the class imbalance problem. More importantly,
our method outperforms the state-of-the-art SSL methods, demonstrating the
potential of our framework for the more challenging SSL setting. Code and
models are available at: https://github.com/xmed-lab/DHC.
