  Using deep learning techniques, anomalies in the paranasal sinus system can
be detected automatically in MRI images and can be further analyzed and
classified based on their volume, shape and other parameters like local
contrast. However due to limited training data, traditional supervised learning
methods often fail to generalize. Existing deep learning methods in paranasal
anomaly classification have been used to diagnose at most one anomaly. In our
work, we consider three anomalies. Specifically, we employ a 3D CNN to separate
maxillary sinus volumes without anomalies from maxillary sinus volumes with
anomalies. To learn robust representations from a small labelled dataset, we
propose a novel learning paradigm that combines contrastive loss and
cross-entropy loss. Particularly, we use a supervised contrastive loss that
encourages embeddings of maxillary sinus volumes with and without anomaly to
form two distinct clusters while the cross-entropy loss encourages the 3D CNN
to maintain its discriminative ability. We report that optimising with both
losses is advantageous over optimising with only one loss. We also find that
our training strategy leads to label efficiency. With our method, a 3D CNN
classifier achieves an AUROC of 0.85 while a 3D CNN classifier optimised with
cross-entropy loss achieves an AUROC of 0.66.
