  Artificial intelligence and machine learning techniques have the promise to
revolutionize the field of digital pathology. However, these models demand
considerable amounts of data, while the availability of unbiased training data
is limited. Synthetic images can augment existing datasets, to improve and
validate AI algorithms. Yet, controlling the exact distribution of cellular
features within them is still challenging. One of the solutions is harnessing
conditional generative adversarial networks that take a semantic mask as an
input rather than a random noise. Unlike other domains, outlining the exact
cellular structure of tissues is hard, and most of the input masks depict
regions of cell types. However, using polygon-based masks introduce inherent
artifacts within the synthetic images - due to the mismatch between the polygon
size and the single-cell size. In this work, we show that introducing random
single-pixel noise with the appropriate spatial frequency into a polygon
semantic mask can dramatically improve the quality of the synthetic images. We
used our platform to generate synthetic images of immunohistochemistry-treated
lung biopsies. We test the quality of the images using a three-fold validation
procedure. First, we show that adding the appropriate noise frequency yields
87% of the similarity metrics improvement that is obtained by adding the actual
single-cell features. Second, we show that the synthetic images pass the Turing
test. Finally, we show that adding these synthetic images to the train set
improves AI performance in terms of PD-L1 semantic segmentation performances.
Our work suggests a simple and powerful approach for generating synthetic data
on demand to unbias limited datasets to improve the algorithms' accuracy and
validate their robustness.
