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

arXiv: https://arxiv.org/abs/2303.06500 · HTML: https://mlsanity.com/paper/2303.06500/

## Key facts

- Cited by 18 papers in the map (data through 14 Oct 2026).
- Builds on 11 of its 30 references that are in the map.
- First posted on arXiv 11 Mar 2023.

## Builds on (most cited first, 11 of 11)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Swin Transformer: Hierarchical Vision Transformer using Shifted Windows](https://mlsanity.com/paper/2103.14030/) (2021)
- [Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks](https://mlsanity.com/paper/1506.01497/) (2015)
- [End-to-End Object Detection with Transformers](https://mlsanity.com/paper/2005.12872/) (2020)
- [Feature Pyramid Networks for Object Detection](https://mlsanity.com/paper/1612.03144/) (2016)
- [SimMIM: A Simple Framework for Masked Image Modeling](https://mlsanity.com/paper/2111.09886/) (2021)
- [DiffusionDet: Diffusion Model for Object Detection](https://mlsanity.com/paper/2211.09788/) (2022)
- [Object Detection with a Unified Label Space from Multiple Datasets](https://mlsanity.com/paper/2008.06614/) (2020)
- [GAIA: A Transfer Learning System of Object Detection that Fits Your Needs](https://mlsanity.com/paper/2106.11346/) (2021)
- [GaNDLF: A Generally Nuanced Deep Learning Framework for Scalable End-to-End Clinical Workflows in Medical Imaging](https://mlsanity.com/paper/2103.01006/) (2021)
- [Individual Tooth Detection and Identification from Dental Panoramic X-Ray Images via Point-wise Localization and Distance Regularization](https://mlsanity.com/paper/2004.05543/) (2020)

## Built on by (most cited first, 18 of 18)

- [Generalist Foundation Models from a Multimodal Dataset for 3D Computed Tomography](https://mlsanity.com/paper/2403.17834/) (2024)
- [CT2Rep: Automated Radiology Report Generation for 3D Medical Imaging](https://mlsanity.com/paper/2403.06801/) (2024)
- [GenerateCT: Text-Conditional Generation of 3D Chest CT Volumes](https://mlsanity.com/paper/2305.16037/) (2023)
- [Holistic Evaluation of GPT-4V for Biomedical Imaging](https://mlsanity.com/paper/2312.05256/) (2023)
- [DentVLM: A Multimodal Vision-Language Model for Comprehensive Dental Diagnosis and Enhanced Clinical Practice](https://mlsanity.com/paper/2509.23344/) (2025)
- [SHiNe: Semantic Hierarchy Nexus for Open-vocabulary Object Detection](https://mlsanity.com/paper/2405.10053/) (2024)
- [PanoGAN A Deep Generative Model for Panoramic Dental Radiographs](https://mlsanity.com/paper/2507.21200/) (2025)
- [SSAD: Self-supervised Auxiliary Detection Framework for Panoramic X-ray based Dental Disease Diagnosis](https://mlsanity.com/paper/2406.13963/) (2024)
- [FD-SOS: Vision-Language Open-Set Detectors for Bone Fenestration and Dehiscence Detection from Intraoral Images](https://mlsanity.com/paper/2407.09088/) (2024)
- [AlphaDent: A dataset for automated tooth pathology detection](https://mlsanity.com/paper/2507.22512/) (2025)
- [YOLOrtho -- A Unified Framework for Teeth Enumeration and Dental Disease Detection](https://mlsanity.com/paper/2308.05967/) (2023)
- [DETDet: Dual Ensemble Teeth Detection](https://mlsanity.com/paper/2308.14070/) (2023)
- [Intergrated Segmentation and Detection Models for Dentex Challenge 2023](https://mlsanity.com/paper/2308.14161/) (2023)
- [Synthesizing Missing MRI Sequences from Available Modalities using Generative Adversarial Networks in BraTS Dataset](https://mlsanity.com/paper/2310.07250/) (2023)
- [3DPX: Single Panoramic X-ray Analysis Guided by 3D Oral Structure Reconstruction](https://mlsanity.com/paper/2409.18701/) (2024)
- [Oral Imaging for Malocclusion Issues Assessments: OMNI Dataset, Deep Learning Baselines and Benchmarking](https://mlsanity.com/paper/2505.15637/) (2025)
- [Exemplar Diffusion: Improving Medical Object Detection with Opportunistic Labels](https://mlsanity.com/paper/2603.15267/) (2026)
- [Topology of a Smile: Persistent Homology in Dental Imaging](https://mlsanity.com/paper/2608.21422/) (2026)

## Abstract (arXiv)

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.