  Volume-wise labeling in 3D medical images is a time-consuming task that
requires expertise. As a result, there is growing interest in using
semi-supervised learning (SSL) techniques to train models with limited labeled
data. However, the challenges and practical applications extend beyond SSL to
settings such as unsupervised domain adaptation (UDA) and semi-supervised
domain generalization (SemiDG). This work aims to develop a generic SSL
framework that can handle all three settings. We identify two main obstacles to
achieving this goal in the existing SSL framework: 1) the weakness of capturing
distribution-invariant features; and 2) the tendency for unlabeled data to be
overwhelmed by labeled data, leading to over-fitting to the labeled data during
training. To address these issues, we propose an Aggregating & Decoupling
framework. The aggregating part consists of a Diffusion encoder that constructs
a common knowledge set by extracting distribution-invariant features from
aggregated information from multiple distributions/domains. The decoupling part
consists of three decoders that decouple the training process with labeled and
unlabeled data, thus avoiding over-fitting to labeled data, specific domains
and classes. We evaluate our proposed framework on four benchmark datasets for
SSL, Class-imbalanced SSL, UDA and SemiDG. The results showcase notable
improvements compared to state-of-the-art methods across all four settings,
indicating the potential of our framework to tackle more challenging SSL
scenarios. Code and models are available at:
https://github.com/xmed-lab/GenericSSL.
