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。