  We propose a novel semi-supervised image segmentation method that
simultaneously optimizes a supervised segmentation and an unsupervised
reconstruction objectives. The reconstruction objective uses an attention
mechanism that separates the reconstruction of image areas corresponding to
different classes. The proposed approach was evaluated on two applications:
brain tumor and white matter hyperintensities segmentation. Our method, trained
on unlabeled and a small number of labeled images, outperformed supervised CNNs
trained with the same number of images and CNNs pre-trained on unlabeled data.
In ablation experiments, we observed that the proposed attention mechanism
substantially improves segmentation performance. We explore two multi-task
training strategies: joint training and alternating training. Alternating
training requires fewer hyperparameters and achieves a better, more stable
performance than joint training. Finally, we analyze the features learned by
different methods and find that the attention mechanism helps to learn more
discriminative features in the deeper layers of encoders.
