  In this paper, we present a self-training-based framework for glaucoma
grading using OCT B-scans under the presence of domain shift. Particularly, the
proposed two-step learning methodology resorts to pseudo-labels generated
during the first step to augment the training dataset on the target domain,
which is then used to train the final target model. This allows transferring
knowledge-domain from the unlabeled data. Additionally, we propose a novel
glaucoma-specific backbone which introduces residual and attention modules via
skip-connections to refine the embedding features of the latent space. By doing
this, our model is capable of improving state-of-the-art from a quantitative
and interpretability perspective. The reported results demonstrate that the
proposed learning strategy can boost the performance of the model on the target
dataset without incurring in additional annotation steps, by using only labels
from the source examples. Our model consistently outperforms the baseline by
1-3% across different metrics and bridges the gap with respect to training the
model on the labeled target data.
