  Semi-supervised learning (SSL) has attracted much attention since it reduces
the expensive costs of collecting adequate well-labeled training data,
especially for deep learning methods. However, traditional SSL is built upon an
assumption that labeled and unlabeled data should be from the same distribution
\textit{e.g.,} classes and domains. However, in practical scenarios, unlabeled
data would be from unseen classes or unseen domains, and it is still
challenging to exploit them by existing SSL methods. Therefore, in this paper,
we proposed a unified framework to leverage these unseen unlabeled data for
open-scenario semi-supervised medical image classification. We first design a
novel scoring mechanism, called dual-path outliers estimation, to identify
samples from unseen classes. Meanwhile, to extract unseen-domain samples, we
then apply an effective variational autoencoder (VAE) pre-training. After that,
we conduct domain adaptation to fully exploit the value of the detected
unseen-domain samples to boost semi-supervised training. We evaluated our
proposed framework on dermatology and ophthalmology tasks. Extensive
experiments demonstrate our model can achieve superior classification
performance in various medical SSL scenarios. The code implementations are
accessible at: https://github.com/PyJulie/USSL4MIC.
