  In the last years, deep learning has dramatically improved the performances
in a variety of medical image analysis applications. Among different types of
deep learning models, convolutional neural networks have been among the most
successful and they have been used in many applications in medical imaging.
  Training deep convolutional neural networks often requires large amounts of
image data to generalize well to new unseen images. It is often time-consuming
and expensive to collect large amounts of data in the medical image domain due
to expensive imaging systems, and the need for experts to manually make ground
truth annotations. A potential problem arises if new structures are added when
a decision support system is already deployed and in use. Since the field of
radiation therapy is constantly developing, the new structures would also have
to be covered by the decision support system.
  In the present work, we propose a novel loss function, that adapts to the
available data in order to utilize all available data, even when some have
missing annotations. We demonstrate that the proposed loss function also works
well in an incremental learning setting, where it can automatically incorporate
new structures as they appear. Experiments on a large in-house data set show
that the proposed method performs on par with baseline models, while greatly
reducing the training time.
