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

翻译：目的：副鼻窦异常在常规放射学筛查中频繁检出，且呈现形态多样性。由于异常形态的多样性，监督学习方法需要包含多种异常形态的大规模标注数据集。自监督学习可从未标注数据中学习表征，但目前尚无针对上颌窦副鼻窦异常分类下游任务设计的自监督学习方法。方法：本文方法采用三维卷积自编码器，在无监督异常检测框架中进行训练。首先训练三维卷积自编码器，使其在重建正常上颌窦图像时最小化重建误差。随后将该自编码器应用于未标注数据集，通过生成残差图像获得粗粒度异常定位。接着使用三维卷积神经网络重建这些残差图像，构成自监督学习任务。最后在包含正常与异常上颌窦图像的标注数据集上对三维卷积神经网络的编码器部分进行微调。结果：与现有通用自监督方法相比，本文提出的自监督学习技术在标注数据有限的情况下展现出更优性能。当仅使用标注数据集的10%进行训练时，本文方法在下游分类任务中达到0.79的精确率-召回率曲线下面积。此性能优于其他方法（BYOL的AUPRC为0.75，SimSiam为0.74，SimCLR为0.73，基于SparK的掩码自编码为0.75）。结论：内嵌副鼻窦异常定位机制的自监督学习方法，对于区分正常与异常上颌窦的后续任务具有显著优势。代码访问地址：https://github.com/mtec-tuhh/self-supervised-paranasal-anomaly