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Item type:Publication, Access status: with files , Self-supervised learning for classifying paranasal anomalies in the maxillary sinus(Springer, 2024-06-08); ; ; ; 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.Publicationtype: Journal Article14 59 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Access status: without files , Convolutional transformer network for paranasal anomaly classification in the maxillary sinus(SPIE, 2024-02); ; ; ; 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.Publicationtype: Conference Paper26 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Access status: with files , Multiple instance ensembling for paranasal anomaly classification in the maxillary sinus(Springer, 2023-07-21); ; ; ; 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 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. 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 relevant MS volume, 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.Publicationtype: Journal ArticleScopus© Citations 2 27 104