BioGAN: An unpaired GAN-based image to image translation model for
  microbiological images
Saber Mirzaee Bafti Chee Siang Ang Gianluca Marcelli Md. Moinul Hossain Sadiya Maxamhud Anastasios D. Tsaousis
A diversified dataset is crucial for training a well-generalized supervised
computer vision algorithm. However, in the field of microbiology, generation
and annotation of a diverse dataset including field-taken images are time
consuming, costly, and in some cases impossible. Image to image translation
frameworks allow us to diversify the dataset by transferring images from one
domain to another. However, most existing image translation techniques require
a paired dataset (original image and its corresponding image in the target
domain), which poses a significant challenge in collecting such datasets. In
addition, the application of these image translation frameworks in microbiology
is rarely discussed. In this study, we aim to develop an unpaired GAN-based
(Generative Adversarial Network) image to image translation model for
microbiological images, and study how it can improve generalization ability of
object detection models. In this paper, we present an unpaired and unsupervised
image translation model to translate laboratory-taken microbiological images to
field images, building upon the recent advances in GAN networks and Perceptual
loss function. We propose a novel design for a GAN model, BioGAN, by utilizing
Adversarial and Perceptual loss in order to transform high level features of
laboratory-taken images into field images, while keeping their spatial
features. The contribution of Adversarial and Perceptual loss in the generation
of realistic field images were studied. We used the synthetic field images,
generated by BioGAN, to train an object-detection framework, and compared the
results with those of an object-detection framework trained with laboratory
images; this resulted in up to 68.1% and 75.3% improvement on F1-score and mAP,
respectively. Codes is publicly available at
https://github.com/Kahroba2000/BioGAN.