Self-supervised learning for classifying paranasal anomalies in the maxillary sinus

arXiv:2404.18599v1 Announce Type: cross
Abstract: 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 …

abstract anomaly arxiv cs.cv data dataset diverse diversity eess.iv however learn self-supervised learning ssl supervised learning type