Dataset and codes for the paper "No-reference quality assessment of dermoscopic images using minimal expert supervision"
Published: 9 February 2026| Version 1 | DOI: 10.17632/2ryw3hpb6v.1
Contributors:
Andrea Ferraris, Francesco Branciforti, Kristen Meiburger, Federica Veronese, Elisa Zavattaro, Paola Savoia, Massimo SalviDescription
Dataset and Source Code for DermaIQA: No-Reference Quality Assessment of Dermoscopic Images This repository contains: Test Dataset - 150 high-quality dermoscopic images - 150 low-quality dermoscopic images Inference Code - Python implementation of DermaIQA - Inference pipeline for quality assessment - Requirements and dependencies - Usage examples (inference.py) Associated with the paper: Ferraris A., Branciforti F., Meiburger K., Veronese F., Zavattaro E., Savoia P., and Salvi M., "No-reference quality assessment of dermoscopic images using minimal expert supervision", Applied Sciences, 2026. Note: The training code and dataset generation pipeline are available upon reasonable request to the corresponding author.
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Categories
Artificial Intelligence, Biomedical Engineering, Dermatology