Automatic thin-prep cytologic test (TCT) screening can assist pathologists in
finding cervical abnormality towards accurate and efficient cervical cancer
diagnosis. Current automatic TCT screening systems mostly involve abnormal
cervical cell detection, which generally requires large-scale and diverse
training data with high-quality annotations to achieve promising performance.
Pathological image synthesis is naturally raised to minimize the efforts in
data collection and annotation. However, it is challenging to generate
realistic large-size cytopathological images while simultaneously synthesizing
visually plausible appearances for small-size abnormal cervical cells. In this
paper, we propose a two-stage image synthesis framework to create synthetic
data for augmenting cervical abnormality screening. In the first Global Image
Generation stage, a Normal Image Generator is designed to generate
cytopathological images full of normal cervical cells. In the second Local Cell
Editing stage, normal cells are randomly selected from the generated images and
then are converted to different types of abnormal cells using the proposed
Abnormal Cell Synthesizer. Both Normal Image Generator and Abnormal Cell
Synthesizer are built upon the pre-trained Stable Diffusion via
parameter-efficient fine-tuning methods for customizing cytopathological image
contents and extending spatial layout controllability, respectively. Our
experiments demonstrate the synthetic image quality, diversity, and
controllability of the proposed synthesis framework, and validate its data
augmentation effectiveness in enhancing the performance of abnormal cervical
cell detection.

通过两阶段图像合成框架，本论文提出了一种用于增强宫颈异常筛查的合成数据方法，包括生成充满正常宫颈细胞的细胞病理图像和将正常细胞转化为不同类型的异常细胞，以提高宫颈异常细胞检测的性能。

用于增强宫颈异常筛查的两阶段细胞病理图像合成

Two-stage Cytopathological Image Synthesis for Augmenting Cervical  Abnormality Screening

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.

本文提出了基于 CellGAN 的 TCT 细胞病理学图像合成方法，用于增强细胞类型分类，并验证了该方法的有效性。