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Self-supervised learning for classifying paranasal anomalies in the maxillary sinus
Citation Link: https://doi.org/10.15480/882.13385
Publikationstyp
Journal Article
Date Issued
2024-06-08
Sprache
English
Author(s)
Bhattacharya, Debayan
TORE-DOI
Volume
19
Issue
9
Start Page
1713
End Page
1721
Citation
International Journal of Computer Assisted Radiology and Surgery 19 (9): 1713–1721 (2024-06-08)
Publisher DOI
Scopus ID
Publisher
Springer
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.
Subjects
Classification
CNN
Maxillary sinus
Paranasal anomaly
Self-supervised learning
MLE@TUHH
DDC Class
610: Medicine, Health
Publication version
publishedVersion
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s11548-024-03172-5.pdf
Type
Main Article
Size
2.85 MB
Format
Adobe PDF