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 when working with 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. In this study, 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 identifying the relevant MS volume within larger head and neck Magnetic Resonance Imaging (MRI) scans can be difficult, but we develop a straightforward strategy to tackle this challenge. Our end-to-end solution includes the use of a novel sampling technique that not only effectively localizes the relevant MS volume, but also increases the size of the training dataset and improves classification results. Additionally, we employ a multiple instance ensemble prediction method to further boost classification performance. Finally, we identify the optimal size of MS volumes to achieve the highest possible classification performance on our dataset. With our multiple instance ensemble prediction strategy and sampling strategy, our 3D CNNs achieve an F1 of 0.85 whereas without it, they achieve an F1 of 0.70. We demonstrate the feasibility of classifying anomalies in the MS. We propose a data enlarging strategy alongside a novel ensembling strategy that proves to be beneficial for paranasal anomaly classification in the MS.

翻译：副鼻窦异常在常规放射学筛查中常见，其形态学特征多样。这种多样性使得卷积神经网络（CNN）在有限数据集下准确分类这些异常面临挑战。此外，当前副鼻窦异常分类方法局限于单次识别一个异常。这些挑战凸显了该领域进一步研究的必要性。本研究探究使用三维卷积神经网络（CNN）对健康上颌窦（MS）及伴有息肉或囊肿的上颌窦进行分类的可行性。在头部和颈部磁共振成像（MRI）扫描中准确识别相关上颌窦区域具有难度，但我们开发了一种简洁策略应对此挑战。我们的端到端解决方案包括一种新型采样技术，该技术不仅能有效定位相关上颌窦区域，还能扩大训练数据集规模并提升分类结果。此外，我们采用多实例集成预测方法进一步提高分类性能。最终，我们确定了数据集上实现最高分类性能的最优上颌窦区域尺寸。采用多实例集成预测策略与采样策略后，我们的三维CNN实现了0.85的F1分数，而未使用时仅为0.70。我们证明了上颌窦异常分类的可行性，提出的数据扩充策略与新型集成策略对副鼻窦异常分类具有显著促进作用。