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Found 4 papers, 1 papers with code
Self-supervised learning for classifying paranasal anomalies in the maxillary sinus
Lastly, we fine-tune the encoder part of the 3D CNN on a labelled dataset of normal and anomalous MS images.
Multiple Instance Ensembling For Paranasal Anomaly Classification In The Maxillary Sinus
We demonstrate the feasibility of classifying anomalies in the MS. We propose a data enlarging strategy alongside a novel ensembling strategy that proves to be beneficial for paranasal anomaly classification in the MS.
Unsupervised Anomaly Detection of Paranasal Anomalies in the Maxillary Sinus
However, experienced clinicians can segregate between normal samples (healthy maxillary sinus) and anomalous samples (anomalous maxillary sinus) after looking at a few normal samples.
Supervised Contrastive Learning to Classify Paranasal Anomalies in the Maxillary Sinus
Particularly, we use a supervised contrastive loss that encourages embeddings of maxillary sinus volumes with and without anomaly to form two distinct clusters while the cross-entropy loss encourages the 3D CNN to maintain its discriminative ability.
