Scripts, figures, and working notes for our team's participation in ImageCLEFmedical GAN task 2023, part of the ImageCLEF labs at the 14th CLEF Conference, 2023.
Implementation Stack: Python, PyTorch, Scikit-learn.
- Research Paper [PDF] describing the methods, rationale, and results.
- Contest Description and Resources.
- Model Pipelines.
If you find our work useful in your research, don't forget to cite us!
@article{hb2023correlating,
url = {https://ceur-ws.org/Vol-3497/paper-116.pdf},
title={Correlating Biomedical Image Fingerprints between GAN-generated and Real Images using a ResNet Backbone with ML-based Downstream Comparators and Clustering: ImageCLEFmed GANs, 2023},
author={Bharathi, Haricharan and Bhaskar, Anirudh and Venkataramani, Vishal and Desingu, Karthik and Kalinathan, Lekshmi},
year={2023},
keywords={Generative Adversarial Network, Support Vector Machines, Heirarchical Clustering, Machine Learning, Deep Learning, ResNet, Convolutional Neural Networks, Few shot learning, Relational model
},
journal={Conference and Labs of the Evaluation Forum},
publisher={Conference and Labs of the Evaluation Forum},
ISSN={1613-0073},
copyright = {Creative Commons Attribution 4.0 International}
}
- A relation neural network based on few-shot learning to capture the underlying similarities between real and artificial images.
- The network learns a tailored difference function to effectively compare images for artificialness arising from GAN-based generation.
- For comparison, hierarchical clustering is used to evaluate the quality of image feature separation between real and artificial images.
- The proposed relational network achieves a 61.4% F1-score in distinguishing real and artificial medical images on a blinded test-set provided and evaluated by ImageCLEF.
See research note and contest page for more information.