Convolutional transformer network for paranasal anomaly classification in the maxillary sinus

Debayan Bhattacharya; Finn Behrendt; Lennart Maack; Benjamin Tobias Becker; Dirk Beyersdorff; Elina Petersen; Marvin Petersen; Bastian Cheng; Dennis Eggert; Christian Betz; Anna Sophie Hoffmann; Alexander Schlaefer

doi:10.1117/12.3005515

3 April 2024 Convolutional transformer network for paranasal anomaly classification in the maxillary sinus

Debayan Bhattacharya, Finn Behrendt, Lennart Maack, Benjamin Tobias Becker, Dirk Beyersdorff, Elina Petersen, Marvin Petersen, Bastian Cheng, Dennis Eggert, Christian Betz, Anna Sophie Hoffmann, Alexander Schlaefer

Author Affiliations +

Proceedings Volume 12927, Medical Imaging 2024: Computer-Aided Diagnosis; 1292717 (2024) https://doi.org/10.1117/12.3005515
Event: SPIE Medical Imaging, 2024, San Diego, California, United States

Abstract

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.

Conference Presentation

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Debayan Bhattacharya, Finn Behrendt, Lennart Maack, Benjamin Tobias Becker, Dirk Beyersdorff, Elina Petersen, Marvin Petersen, Bastian Cheng, Dennis Eggert, Christian Betz, Anna Sophie Hoffmann, and Alexander Schlaefer "Convolutional transformer network for paranasal anomaly classification in the maxillary sinus", Proc. SPIE 12927, Medical Imaging 2024: Computer-Aided Diagnosis, 1292717 (3 April 2024); https://doi.org/10.1117/12.3005515

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

;

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE