  Purpose: Paranasal anomalies, frequently identified in routine radiological
screenings, exhibit diverse morphological characteristics. Due to the diversity
of anomalies, supervised learning methods require large labelled dataset
exhibiting diverse anomaly morphology. Self-supervised learning (SSL) can be
used to learn representations from unlabelled data. However, there are no SSL
methods designed for the downstream task of classifying paranasal anomalies in
the maxillary sinus (MS).
  Methods: Our approach uses a 3D Convolutional Autoencoder (CAE) trained in an
unsupervised anomaly detection (UAD) framework. Initially, we train the 3D CAE
to reduce reconstruction errors when reconstructing normal maxillary sinus (MS)
image. Then, this CAE is applied to an unlabelled dataset to generate coarse
anomaly locations by creating residual MS images. Following this, a 3D
Convolutional Neural Network (CNN) reconstructs these residual images, which
forms our SSL task. Lastly, we fine-tune the encoder part of the 3D CNN on a
labelled dataset of normal and anomalous MS images.
  Results: The proposed SSL technique exhibits superior performance compared to
existing generic self-supervised methods, especially in scenarios with limited
annotated data. When trained on just 10% of the annotated dataset, our method
achieves an Area Under the Precision-Recall Curve (AUPRC) of 0.79 for the
downstream classification task. This performance surpasses other methods, with
BYOL attaining an AUPRC of 0.75, SimSiam at 0.74, SimCLR at 0.73 and Masked
Autoencoding using SparK at 0.75.
  Conclusion: A self-supervised learning approach that inherently focuses on
localizing paranasal anomalies proves to be advantageous, particularly when the
subsequent task involves differentiating normal from anomalous maxillary
sinuses. Access our code at
https://github.com/mtec-tuhh/self-supervised-paranasal-anomaly
