Pair-GAN: A Three-Validated Generative Model from Single Pairs of Biomedical and Ground Truth Images

Clara Brémond-Martin, Huaqian Wu, Cédric Clouchoux, Kévin François-Bouaou

2024

Abstract

Generating synthetic pairs of raw and ground truth (GT) image is a strategy to reduce the amount of acquisition and annotation by biomedical experts. Pair image generation strategies, from single-input paired images (SIP), focus on patch-pyramid (PP) or on dual branch generator but, resulting synthetic images are not natural. With few-input images, for raw synthesis, adversarial auto-encoders synthesises more natural images. Here we propose Pair-GAN, a combination of PP containing auto-encoder generators at each level, for the biomedical image synthesis based upon a SIP. PP allows to synthesise using SIP while the AAE generator renders most natural the image content. We use for this work two biomedical datasets containing raw and GT images. Our architecture is evaluated with seven state of the art method updated for SIP: qualitative, similitude and segmentation metrics, Kullback Leibler divergences from synthetic and original feature image representations, computational costs and statistical analyses. Pair-GAN generates most qualitative and natural outputs, similar to original pairs with complex shape not produced by other methods, however with increased memory needs. Future works may use this generative procedure for multimodal biomedical dataset synthesis to help their automatic processing such as classification or segmentation with deep learning tools.

Paper Citation

in Harvard Style

Brémond-Martin C., Wu H., Clouchoux C. and François-Bouaou K. (2024). Pair-GAN: A Three-Validated Generative Model from Single Pairs of Biomedical and Ground Truth Images. In Proceedings of the 19th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - Volume 3: VISAPP; ISBN 978-989-758-679-8, SciTePress, pages 37-52. DOI: 10.5220/0012318300003660

in Bibtex Style

@conference{visapp24,
author={Clara Brémond-Martin and Huaqian Wu and Cédric Clouchoux and Kévin François-Bouaou},
title={Pair-GAN: A Three-Validated Generative Model from Single Pairs of Biomedical and Ground Truth Images},
booktitle={Proceedings of the 19th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - Volume 3: VISAPP},
year={2024},
pages={37-52},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0012318300003660},
isbn={978-989-758-679-8},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 19th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - Volume 3: VISAPP
TI - Pair-GAN: A Three-Validated Generative Model from Single Pairs of Biomedical and Ground Truth Images
SN - 978-989-758-679-8
AU - Brémond-Martin C.
AU - Wu H.
AU - Clouchoux C.
AU - François-Bouaou K.
PY - 2024
SP - 37
EP - 52
DO - 10.5220/0012318300003660
PB - SciTePress