DermaSynth: Rich Synthetic Image-Text Pairs Using Open Access Dermatology Datasets

Abdurrahim Yilmaz, Furkan Yuceyalcin, Ece Gokyayla, Donghee Choi, Ozan Erdem +6 more
1/31/2025
cs.CVcs.AIcs.CL

Official code: abdurrahimyilmaz/DermaSynth (7 stars, last commit 2025-02-08).

Abstract

A major barrier to developing vision large language models (LLMs) in dermatology is the lack of large image--text pairs dataset. We introduce DermaSynth, a dataset comprising of 92,020 synthetic image--text pairs curated from 45,205 images (13,568 clinical and 35,561 dermatoscopic) for dermatology-related clinical tasks. Leveraging state-of-the-art LLMs, using Gemini 2.0, we used clinically related prompts and self-instruct method to generate diverse and rich synthetic texts. Metadata of the datasets were incorporated into the input prompts by targeting to reduce potential hallucinations. The resulting dataset builds upon open access dermatological image repositories (DERM12345, BCN20000, PAD-UFES-20, SCIN, and HIBA) that have permissive CC-BY-4.0 licenses. We also fine-tuned a preliminary Llama-3.2-11B-Vision-Instruct model, DermatoLlama 1.0, on 5,000 samples. We anticipate this dataset to support and accelerate AI research in dermatology. Data and code underlying this work are accessible at https://github.com/abdurrahimyilmaz/DermaSynth.

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Code Implementations(1)

abdurrahimyilmaz/DermaSynthOfficial100%
70Python, Jupyter NotebookFeb 3, 20251 years ago

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Cite this paper

@article{yilmaz2025dermasynth,
  title  = {DermaSynth: Rich Synthetic Image-Text Pairs Using Open Access Dermatology Datasets},
  author = {Abdurrahim Yilmaz and Furkan Yuceyalcin and Ece Gokyayla and Donghee Choi and Ozan Erdem and Ali Anil Demircali and Rahmetullah Varol and Ufuk Gorkem Kirabali and Gulsum Gencoglan and Joram M. Posma and Burak Temelkuran},
  year   = {2025},
  eprint = {2502.00196},
  archivePrefix = {arXiv},
  url    = {http://arxiv.org/abs/2502.00196v2}
}

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