SBMN: Similarity-Based Memory Network for the Diagnosis of Vertical Root Fracture in Dental Imaging

TL;DR

This paper introduces a new neural network called SBMN to improve the diagnosis of dental vertical root fractures using similarity-based learning.

Contribution

The novel SBMN integrates Category Memory and a similarity-based classifier for better small-sample medical image classification.

Findings

SBMN achieved 97.1% accuracy on automatically segmented dental images.

Manual segmentation improved accuracy to 99.7%.

Category Memory was confirmed as critical for classification outcomes.

Abstract

Background/Objectives: Medical image analysis of vertical root fractures (VRFs) is challenged by limited annotated data, class imbalance, and subtle inter-class differences. To address these issues, we propose an SBMN: a Similarity-Based Memory Network that integrates Category Memory with the Basic SBMN Module and a similarity-based classifier. Methods: An SBMN stores representative features for each class and leverages similarity-based gating to enhance feature discrimination. Experiments were conducted on a CBCT dataset of fractured and non-fractured teeth to evaluate performance. Results: The SBMN achieved up to 97.1% and 99.7% classification accuracy on automatically and manually segmented images, respectively. Memory manipulation experiments confirm the critical role of Category Memory in controlling classification outcomes. Conclusions: These results indicate that SBMNs offer an effective and interpretable approach for small-sample medical image classification and diagnosis.

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