Using GANs to synthetically stain histopathological images to generate training data for automatic mitosis detection in breast tissue
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2019-04-29
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en
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Abstract
Generative adversarial networks (GANs) have been proven effective at mapping
medical images from one domain to another (e.g. from CT to MRI).
In this study we investigate the effectiveness of GANs at mapping images of
breast tissue between histopathological stains.
Breast cancer is the most common cancer in women worldwide. Counting
mitotic figures in histological images of breast cancer tissue has been shown
to be a reliable and independent prognostic marker. Most successful methods
for automatic counting involve training deep neural networks on H&E stained
slides. This training requires extensive manual annotations of mitotic figures
in H&E stained slides, which suffers from a low inter-observer agreement.
Manual counting in PHH3 stained slides has a much higher inter-observer
agreement.
In this project we aimed to train GANs to map PHH3 slides to synthetic
H&E slides and vice versa. A mitosis classifier is used to quantify the quality
of the synthetic images, by comparing its performance after training on
synthetic images with training on real images.
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Faculteit der Sociale Wetenschappen