GANs for Biological Image Synthesis

Anton Osokin 1, 2 Anatole Chessel 3 Rafael E. Carazo Salas 4 Federico Vaggi 5, 6
1 WILLOW - Models of visual object recognition and scene understanding
DI-ENS - Département d'informatique de l'École normale supérieure, Inria de Paris
5 SIERRA - Statistical Machine Learning and Parsimony
DI-ENS - Département d'informatique de l'École normale supérieure, CNRS - Centre National de la Recherche Scientifique, Inria de Paris
Abstract : In this paper, we propose a novel application of Generative Adversarial Networks (GAN) to the synthesis of cells imaged by fluorescence microscopy. Compared to natural images, cells tend to have a simpler and more geometric global structure that facilitates image generation. However, the correlation between the spatial pattern of different fluorescent proteins reflects important biological functions, and synthesized images have to capture these relationships to be relevant for biological applications. We adapt GANs to the task at hand and propose new models with casual dependencies between image channels that can generate multi-channel images, which would be impossible to obtain experimentally. We evaluate our approach using two independent techniques and compare it against sensible baselines. Finally, we demonstrate that by interpolating across the latent space we can mimic the known changes in protein localization that occur through time during the cell cycle, allowing us to predict temporal evolution from static images.
Type de document :
Communication dans un congrès
ICCV 2017 - IEEE International Conference on Computer Vision, Oct 2017, Venice, Italy. 2017
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https://hal.archives-ouvertes.fr/hal-01611692
Contributeur : Anton Osokin <>
Soumis le : vendredi 6 octobre 2017 - 11:59:12
Dernière modification le : jeudi 10 mai 2018 - 02:08:15

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  • HAL Id : hal-01611692, version 1
  • ARXIV : 1708.04692

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Anton Osokin, Anatole Chessel, Rafael E. Carazo Salas, Federico Vaggi. GANs for Biological Image Synthesis. ICCV 2017 - IEEE International Conference on Computer Vision, Oct 2017, Venice, Italy. 2017. 〈hal-01611692〉

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