  Medical image segmentation has been significantly advanced by deep learning
(DL) techniques, though the data scarcity inherent in medical applications
poses a great challenge to DL-based segmentation methods. Self-supervised
learning offers a solution by creating auxiliary learning tasks from the
available dataset and then leveraging the knowledge acquired from solving
auxiliary tasks to help better solve the target segmentation task. Different
auxiliary tasks may have different properties and thus can help the target task
to different extents. It is desired to leverage their complementary advantages
to enhance the overall assistance to the target task. To achieve this, existing
methods often adopt a joint training paradigm, which co-solves segmentation and
auxiliary tasks by integrating their losses or intermediate gradients. However,
direct coupling of losses or intermediate gradients risks undesirable
interference because the knowledge acquired from solving each auxiliary task at
every training step may not always benefit the target task. To address this
issue, we propose a two-stage training approach. In the first stage, the target
segmentation task will be independently co-solved with each auxiliary task in
both joint training and pre-training modes, with the better model selected via
validation performance. In the second stage, the models obtained with respect
to each auxiliary task are converted into a single model using an ensemble
knowledge distillation method. Our approach allows for making best use of each
auxiliary task to create multiple elite segmentation models and then combine
them into an even more powerful model. We employed five auxiliary tasks of
different proprieties in our approach and applied it to train the U-Net model
on an X-ray pneumothorax segmentation dataset. Experimental results demonstrate
the superiority of our approach over several existing methods.
