ImageCLEF Medical GANs 2023

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.

Quick Links

• Research Paper [PDF] describing the methods, rationale, and results.
• Contest Description and Resources.
• Model Pipelines.

Cite Us

Link to the Research Paper.

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}
}

Key Highlights

• A relation neural network based on few-shot learning to capture the underlying similarities between real and artificial images.
• The network learns a custom difference function to effectively compare images for artificialness arising from GAN-based generation.
• For comparison, it uses hierarchical clustering to evaluate the quality of image feature separation between real and artificial images.
• The proposed relation network achieves a 61.4% F1-score in distinguishing real and artificial medical images on a blinded contest test set.

See the paper and contest page for more information.