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

TL;DR

This paper introduces a novel diffusion-based hierarchical multi-label object detection framework for panoramic dental X-rays, enabling simultaneous identification of problematic teeth, their enumeration, and diagnoses, leveraging hierarchically annotated data.

Contribution

The paper presents a new diffusion-based method that effectively learns from hierarchically and partially annotated dental X-ray datasets for improved object detection.

Findings

Outperforms state-of-the-art detection methods like RetinaNet, Faster R-CNN, DETR, and DiffusionDet.

Effectively utilizes hierarchically annotated data for multi-label detection.

Demonstrates potential for clinical dental diagnosis applications.

Abstract

Due to the necessity for precise treatment planning, the use of panoramic X-rays to identify different dental diseases has tremendously increased. Although numerous ML models have been developed for the interpretation of panoramic X-rays, there has not been an end-to-end model developed that can identify problematic teeth with dental enumeration and associated diagnoses at the same time. To develop such a model, we structure the three distinct types of annotated data hierarchically following the FDI system, the first labeled with only quadrant, the second labeled with quadrant-enumeration, and the third fully labeled with quadrant-enumeration-diagnosis. To learn from all three hierarchies jointly, we introduce a novel diffusion-based hierarchical multi-label object detection framework by adapting a diffusion-based method that formulates object detection as a denoising diffusion process from noisy boxes to object boxes. Specifically, to take advantage of the hierarchically annotated data, our method utilizes a novel noisy box manipulation technique by adapting the denoising process in the diffusion network with the inference from the previously trained model in hierarchical order. We also utilize a multi-label object detection method to learn efficiently from partial annotations and to give all the needed information about each abnormal tooth for treatment planning. Experimental results show that our method significantly outperforms 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 and partially annotated datasets. The code and the data are available at: https://github.com/ibrahimethemhamamci/HierarchicalDet.