CellGAN: Conditional Cervical Cell Synthesis for Augmenting
  Cytopathological Image Classification
Zhenrong Shen Maosong Cao Sheng Wang Lichi Zhang Qian Wang
Automatic examination of thin-prep cytologic test (TCT) slides can assist
pathologists in finding cervical abnormality for accurate and efficient cancer
screening. Current solutions mostly need to localize suspicious cells and
classify abnormality based on local patches, concerning the fact that whole
slide images of TCT are extremely large. It thus requires many annotations of
normal and abnormal cervical cells, to supervise the training of the
patch-level classifier for promising performance. In this paper, we propose
CellGAN to synthesize cytopathological images of various cervical cell types
for augmenting patch-level cell classification. Built upon a lightweight
backbone, CellGAN is equipped with a non-linear class mapping network to
effectively incorporate cell type information into image generation. We also
propose the Skip-layer Global Context module to model the complex spatial
relationship of the cells, and attain high fidelity of the synthesized images
through adversarial learning. Our experiments demonstrate that CellGAN can
produce visually plausible TCT cytopathological images for different cell
types. We also validate the effectiveness of using CellGAN to greatly augment
patch-level cell classification performance.