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Imaging Biological Samples with Optical Microscopy01:18

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Fine-Tuning a Small Vision Language Model Using Synthetic Data for Explaining Bacterial Skin Disease Images.

Shiwan Zhang1, Abdurrahim Yilmaz2, Gulsum Gencoglan3

  • 1Hamlyn Centre, Department of Surgery and Cancer, Imperial College London, London SW7 2AZ, UK.

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Summary

Compact vision language models (VLMs) show promise for dermatology. Synthetic question-answer data improved a small VLM

Keywords:
bacterial skin diseasesdermatology imagingfine-tuningmedical AIsynthetic datavision language modelsvisual question answering

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Area of Science:

  • Artificial Intelligence in Medicine
  • Dermatology
  • Medical Image Analysis

Background:

  • Vision language models (VLMs) offer potential for medical image understanding.
  • Large VLM scale hinders practical deployment in clinical settings.
  • Adapting compact VLMs for specialized medical domains like dermatology is crucial.

Purpose of the Study:

  • To investigate the effectiveness of a compact VLM for bacterial skin disease image understanding.
  • To evaluate the impact of different supervision strategies on VLM performance.
  • To assess the utility of synthetic question-answer (QA) data for VLM fine-tuning.

Main Methods:

  • Curated a dermatology-specific dataset (PMC-derma-VQA-bacteria) from PMC-OA and BIOMEDICA.
  • Generated synthetic QA pairs using Google's Gemini model.
  • Fine-tuned a compact VLM (SmolVLM) using QA-only, caption-only, and combined QA+caption supervision.

Main Results:

  • QA-only supervision achieved superior report-generation quality.
  • The combined QA+caption strategy resulted in the highest diagnostic classification accuracy (70.20%).
  • Synthetic QA supervision significantly enhanced VLM performance.

Conclusions:

  • Compact VLMs can be effectively adapted for dermatology using targeted fine-tuning.
  • Synthetic QA data is a viable method to improve VLM capabilities in medical image analysis.
  • This approach supports enhanced diagnostic support in dermatology.