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Batch equalization with a generative adversarial network.
Qian, Wesley Wei; Xia, Cassandra; Venugopalan, Subhashini; Narayanaswamy, Arunachalam; Dimon, Michelle; Ashdown, George W; Baum, Jake; Peng, Jian; Ando, D Michael.
Afiliação
  • Qian WW; Department of Computer Science, University of Illinois at Urbana-Champaign, Urbana 61801, IL, USA.
  • Xia C; Google Research, 1600 Amphitheatre Parkway Mountain View, CA 94043.
  • Venugopalan S; Google Research, 1600 Amphitheatre Parkway Mountain View, CA 94043.
  • Narayanaswamy A; Google Research, 1600 Amphitheatre Parkway Mountain View, CA 94043.
  • Dimon M; Google Research, 1600 Amphitheatre Parkway Mountain View, CA 94043.
  • Ashdown GW; Department of Life Sciences, Imperial College London, London SW7 2AZ, UK.
  • Baum J; Department of Life Sciences, Imperial College London, London SW7 2AZ, UK.
  • Peng J; Department of Computer Science, University of Illinois at Urbana-Champaign, Urbana 61801, IL, USA.
  • Ando DM; Google Research, 1600 Amphitheatre Parkway Mountain View, CA 94043.
Bioinformatics ; 36(Suppl_2): i875-i883, 2020 12 30.
Article em En | MEDLINE | ID: mdl-33381813
ABSTRACT
MOTIVATION Advances in automation and imaging have made it possible to capture a large image dataset that spans multiple experimental batches of data. However, accurate biological comparison across the batches is challenged by batch-to-batch variation (i.e. batch effect) due to uncontrollable experimental noise (e.g. varying stain intensity or cell density). Previous approaches to minimize the batch effect have commonly focused on normalizing the low-dimensional image measurements such as an embedding generated by a neural network. However, normalization of the embedding could suffer from over-correction and alter true biological features (e.g. cell size) due to our limited ability to interpret the effect of the normalization on the embedding space. Although techniques like flat-field correction can be applied to normalize the image values directly, they are limited transformations that handle only simple artifacts due to batch effect.

RESULTS:

We present a neural network-based batch equalization method that can transfer images from one batch to another while preserving the biological phenotype. The equalization method is trained as a generative adversarial network (GAN), using the StarGAN architecture that has shown considerable ability in style transfer. After incorporating new objectives that disentangle batch effect from biological features, we show that the equalized images have less batch information and preserve the biological information. We also demonstrate that the same model training parameters can generalize to two dramatically different types of cells, indicating this approach could be broadly applicable. AVAILABILITY AND IMPLEMENTATION https//github.com/tensorflow/gan/tree/master/tensorflow_gan/examples/stargan. SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.
Assuntos

Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Assunto principal: Processamento de Imagem Assistida por Computador / Redes Neurais de Computação Idioma: En Ano de publicação: 2020 Tipo de documento: Article

Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Assunto principal: Processamento de Imagem Assistida por Computador / Redes Neurais de Computação Idioma: En Ano de publicação: 2020 Tipo de documento: Article