# AlphaDent: A dataset for automated tooth pathology detection

arXiv: https://arxiv.org/abs/2507.22512 · HTML: https://mlsanity.com/paper/2507.22512/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 9 of its 30 references that are in the map.
- First posted on arXiv 30 Jul 2025.

## Builds on (most cited first, 9 of 9)

- [Microsoft COCO: Common Objects in Context](https://mlsanity.com/paper/1405.0312/) (2014)
- [Mask R-CNN](https://mlsanity.com/paper/1703.06870/) (2017)
- [Masked-attention Mask Transformer for Universal Image Segmentation](https://mlsanity.com/paper/2112.01527/) (2021)
- [Hybrid Task Cascade for Instance Segmentation](https://mlsanity.com/paper/1901.07518/) (2019)
- [YOLACT: Real-time Instance Segmentation](https://mlsanity.com/paper/1904.02689/) (2019)
- [OneFormer: One Transformer to Rule Universal Image Segmentation](https://mlsanity.com/paper/2211.06220/) (2022)
- [A Comprehensive Review of YOLO Architectures in Computer Vision: From YOLOv1 to YOLOv8 and YOLO-NAS](https://mlsanity.com/paper/2304.00501/) (2023)
- [Diffusion-Based Hierarchical Multi-Label Object Detection to Analyze Panoramic Dental X-rays](https://mlsanity.com/paper/2303.06500/) (2023)
- [Teeth3DS+: An Extended Benchmark for Intraoral 3D Scans Analysis](https://mlsanity.com/paper/2210.06094/) (2022)

## Built on by (most cited first, 2 of 2)

- [DentalGPT: Incentivizing Multimodal Complex Reasoning in Dentistry](https://mlsanity.com/paper/2512.11558/) (2025)
- [DinoDental: Benchmarking DINOv3 as a Unified Vision Encoder for Dental Image Analysis](https://mlsanity.com/paper/2603.28297/) (2026)

## Abstract (arXiv)

In this article, we present a new unique dataset for dental research - AlphaDent. This dataset is based on the DSLR camera photographs of the teeth of 295 patients and contains over 1200 images. The dataset is labeled for solving the instance segmentation problem and is divided into 9 classes. The article provides a detailed description of the dataset and the labeling format. The article also provides the details of the experiment on neural network training for the Instance Segmentation problem using this dataset. The results obtained show high quality of predictions. The dataset is published under an open license; and the training/inference code and model weights are also available under open licenses.