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

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 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

SUBMITTER: Bhattacharya D 

PROVIDER: S-EPMC11365849 | biostudies-literature | 2024 Sep

REPOSITORIES: biostudies-literature