DiMNet: Multi-Label Detection Algorithm for Panoramic Radiographs

In recent years, deep learning has been widely applied to single-target detec-tion tasks in dental images, achieving promising results. Existing methods aiming to achieve multi-label detection rely heavily on fully annotated data. However, due to the difficulty in obtaining such fully annotated data, the de-tection accuracy remains low, failing to meet the requirements of clinical di-agnosis. To address this limitation, we propose DiMNet, a end-to-end multi-label object detection model based on an improved DiffusionDet, which in-corporates multi-stage training, weight transfer, and cross-stage guidance to enable the model to be trained on partially annotated data, thereby improving detection accuracy. Additionally, we enhance the feature extraction backbone by integrating the Mamba model, leveraging its linear-time sequence model-ing approach to maintain high accuracy while significantly improving infer-ence speed. The model is capable of identifying dental pathologies in pano-ramic X-ray images while simultaneously providing the quadrant and tooth number of the affected tooth, maintaining high accuracy and fast inference speed, thereby meeting the requirements of fully automated diagnosis. During the experiments, we utilized DENTEX2023, which features a multi-level structure, enabling a comprehensive evaluation of the effectiveness of the proposed improvements in DiMNet. Experimental results demonstrate that DiMNet achieves AR scores of 71.7 for quadrant detection, 66.8 for enumeration, and 69.1 for dental pathology detection on the test dataset, accurately detecting all three targets in dental images simultaneously.