Multiple instance ensembling for paranasal anomaly classification in the maxillary sinus

Purpose Paranasal anomalies are commonly discovered during routine radiological screenings and can present with a wide
range of morphological features. This diversity can make it difficult for convolutional neural networks (CNNs) to accurately
classify these anomalies, especially whenworkingwith limited datasets. Additionally, current approaches to paranasal anomaly
classification are constrained to identifying a single anomaly at a time. These challenges necessitate the need for further
research and development in this area.
Methods We investigate the feasibility of using a 3D convolutional neural network (CNN) to classify healthy maxillary
sinuses (MS) and MS with polyps or cysts. The task of accurately localizing the relevant MS volume within larger head and
neck Magnetic Resonance Imaging (MRI) scans can be difficult, but we develop a strategy which includes the use of a novel
sampling technique that not only effectively localizes the relevantMSvolume, but also increases the size of the training dataset
and improves classification results. Additionally, we employ a Multiple Instance Ensembling (MIE) prediction method to
further boost classification performance.
Results With sampling and MIE, we observe that there is consistent improvement in classification performance of all 3D
ResNet and 3D DenseNet architecture with an average AUPRC percentage increase of 21.86 ± 11.92% and 4.27 ± 5.04%
by sampling and 28.86 ± 12.80% and 9.85 ± 4.02% by sampling and MIE, respectively.
Conclusion Sampling and MIE can be effective techniques to improve the generalizability of CNNs for paranasal anomaly
classification.We demonstrate the feasibility of classifying anomalies in the MS.We propose a data enlarging strategy through
sampling alongside a novel MIE strategy that proves to be beneficial for paranasal anomaly classification in the MS.