README.md

---
license: cc-by-nc-4.0
language:
- en
task_categories:
- image-text-to-text
- visual-question-answering
- image-classification
tags:
- dental
- medical
- multimodal
- vision-language
- vqa
- pall
size_categories:
- 10K<n<100K
---

# PALL-VLM-data — Dental Vision-Language Dataset

The training dataset for [`Harisundar/PALL-VLM`](https://huggingface.co/Harisundar/PALL-VLM),
a dental vision-language model. It contains **32,884 records** over **52,461 images**,
formatted as image+text conversations for LLaVA-style instruction tuning.

- **Curated by:** Harisundar R
- **Used by:** [`Harisundar/PALL-VLM`](https://huggingface.co/Harisundar/PALL-VLM) · [PALL on GitHub](https://github.com/HARISUNDARRAJENDRAN/PALL)
- **Language:** English

## Layout

```
vlm_train/
├── images/                  # 52,461 dental images
├── train.jsonl              # 29,667 records
├── val.jsonl                #  1,640 records
├── test.jsonl              #  1,577 records
└── manifest.json            # provenance / split & source distribution
```

## Schema

Each JSONL row is a conversation referencing one or more images:

```json
{
  "id": "...",
  "source": "oral_cancer_photos_pq",
  "task_type": "vqa",
  "messages": [
    {"role": "user", "content": "<image>\n<question>"},
    {"role": "assistant", "content": "<answer>"}
  ],
  "images": ["images/rel/path.jpg"]
}
```

The number of `<image>` tokens in the user turn always equals `len(images)`
(single- and multi-image rows; **7,228** records are multi-image).

## Composition

**Splits:** train 29,667 · val 1,640 · test 1,577.

**Task subtypes:** classification 23,728 · detection 2,564 · caption 1,231 · report 998 ·
segmentation 562 · (other 3,801).

---

## Source Attribution & Citations

This dataset was assembled from multiple publicly available dental image datasets and sources.
We gratefully acknowledge the original creators.

| Source | Records | Task(s) | Attribution |
|--------|--------:|---------|-------------|
| Oral cancer clinical photos (PQ) | 10,002 | classification | Kaggle oral cancer image dataset contributors |
| CODE oral classification | 7,546 | classification | CODE oral lesion classification dataset |
| Oral cancer histopathology | 5,127 | classification | Community histopathology datasets |
| Dental textbook figures | 3,221 | VQA, caption | Various textbook authors (see [PALL-Text](https://huggingface.co/Harisundar/PALL-Text) card) |
| Radiograph caries (ICDAS) | 1,431 | classification, detection | ICDAS Foundation; Ismail, A.I. et al. (2007). *The International Caries Detection and Assessment System (ICDAS).* Community Dentistry and Oral Epidemiology, 35(3), 170–178 |
| Dental samples | 1,082 | mixed | Community dental image datasets |
| SMART oral photos | 1,071 | classification | SMART oral lesion dataset contributors |
| **Tufts Dental Database** | 998 | report generation | Panetta, K., Rajendran, R., Ramesh, A., Rao, S., & Agaian, S. (2022). *Tufts Dental Database.* IEEE J. Biomed. Health Inform., 26(4), 1650–1659 |
| **DENTEX** — quadrant detection | 676 | detection | Hamamci, I.E. et al. (2023). *DENTEX: An Abnormal Tooth Detection with Dental Enumeration and Diagnosis Benchmark for Panoramic X-rays.* arXiv:2305.19112 |
| Dental radiology | 580 | classification | Community dental radiology datasets |
| Oral cancer clinical photos (2) | 544 | classification | Kaggle oral cancer datasets |
| **DENTEX** — disease classification | 407 | classification | Hamamci, I.E. et al. (2023) (same as above) |
| Dental jaw captions | 144 | captioning | Community dental datasets |
| **DENTEX** — enumeration | 50 | enumeration | Hamamci, I.E. et al. (2023) (same as above) |
| Dental image dataset | 5 | mixed | Community contribution |

### BibTeX citations for key image dataset sources

```bibtex
@article{panetta2022tufts,
  title={Tufts Dental Database: A Multimodal Panoramic X-Ray Dataset for Benchmarking Diagnostic Systems},
  author={Panetta, Karen and Rajendran, Rahul and Ramesh, Aruna and Rao, Shishir and Agaian, Sos},
  journal={IEEE Journal of Biomedical and Health Informatics},
  volume={26}, number={4}, pages={1650--1659}, year={2022}, doi={10.1109/JBHI.2021.3117575}
}
@article{hamamci2023dentex,
  title={DENTEX: An Abnormal Tooth Detection with Dental Enumeration and Diagnosis Benchmark for Panoramic X-rays},
  author={Hamamci, Ibrahim Ethem and Er, Sezgin and Simsar, Enis and Sekuboyina, Anjany
          and Gundogar, Mustafa and Stadlinger, Bernd and Mehl, Albert and Menze, Bjoern},
  journal={arXiv preprint arXiv:2305.19112}, year={2023}
}
@article{ismail2007icdas,
  title={The International Caries Detection and Assessment System (ICDAS): an integrated system for measuring dental caries},
  author={Ismail, Amid I. and Sohn, Woosung and Tellez, Marisol and Amaya, Ashley
          and Sen, Ananda and Hasson, Hana and Pitts, Nigel B.},
  journal={Community Dentistry and Oral Epidemiology}, volume={35}, number={3}, pages={170--178},
  year={2007}, doi={10.1111/j.1600-0528.2007.00347.x}
}
```

---

## Intended use & limitations

- **Intended:** training/evaluating dental vision-language models for education and
  clinical-decision *support*.
- The set is **classification-heavy**; evaluation should include an image-shuffle control to
  guard against modality collapse.
- Wide panoramic radiographs may need tiling/AnyRes for best results (v1 uses square resize).
- **Not** for autonomous diagnosis. Respect the source licenses of the constituent datasets.

## Citation

```bibtex
@misc{rajendran2026pallvlmdata,
  title        = {PALL-VLM-data: A Dental Vision-Language Instruction Dataset},
  author       = {Rajendran, Harisundar},
  year         = {2026},
  howpublished = {\url{https://huggingface.co/datasets/Harisundar/PALL-VLM-data}},

}
```