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Due to the necessity for precise treatment planning, the use of panoramicX-rays to identify different dental diseases has tremendously increased.Although numerous ML models have been developed for the interpretation ofpanoramic X-rays, there has not been an end-to-end model developed that canidentify problematic teeth with dental enumeration and associated diagnoses atthe same time. To develop such a model, we structure the three distinct typesof annotated data hierarchically following the FDI system, the first labeledwith only quadrant, the second labeled with quadrant-enumeration, and the thirdfully labeled with quadrant-enumeration-diagnosis. To learn from all threehierarchies jointly, we introduce a novel diffusion-based hierarchicalmulti-label object detection framework by adapting a diffusion-based methodthat formulates object detection as a denoising diffusion process from noisyboxes to object boxes. Specifically, to take advantage of the hierarchicallyannotated data, our method utilizes a novel noisy box manipulation technique byadapting the denoising process in the diffusion network with the inference fromthe previously trained model in hierarchical order. We also utilize amulti-label object detection method to learn efficiently from partialannotations and to give all the needed information about each abnormal toothfor treatment planning. Experimental results show that our method significantlyoutperforms state-of-the-art object detection methods, including RetinaNet,Faster R-CNN, DETR, and DiffusionDet for the analysis of panoramic X-rays,demonstrating the great potential of our method for hierarchically andpartially annotated datasets. The code and the data are available at:https://github.com/ibrahimethemhamamci/HierarchicalDet.

Diffusion-Based Hierarchical Multi-Label Object Detection to Analyze Panoramic Dental X-rays