Convolutional transformer network for paranasal anomaly classification in the maxillary sinus

Large-scale population studies have examined the detection of sinus opacities in cranial MRIs. Deep learning methods, specifically 3D convolutional neural networks (CNNs), have been used to classify these anomalies. However, CNNs have limitations in capturing long-range dependencies across the low and high level features, potentially reducing performance. To address this, we propose an end-to-end pipeline using a novel deep learning network called ConTra-Net. ConTra-Net combines the strengths of CNNs and self-attention mechanisms of transformers to classify paranasal anomalies in the maxillary sinuses. Our approach outperforms 3D CNNs and 3D Vision Transformer (ViT), with relative improvements in F1 score of 11.68% and 53.5%, respectively. Our pipeline with ConTra-Net could serve as an alternative to reduce misdiagnosis rates in classifying paranasal anomalies.

Subjects

anomaly classification

CNN

Hybrid Network

maxillary sinus

Paranasal anomaly

Transformer

MLE@TUHH

DDC Class

004: Computer Sciences

610: Medicine, Health

620: Engineering

Options

Convolutional transformer network for paranasal anomaly classification in the maxillary sinus