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。

CellGAN: 用于增强细胞病理图像分类的条件性宫颈细胞合成

CellGAN: Conditional Cervical Cell Synthesis for Augmenting  Cytopathological Image Classification

Convolutional Neural Networks (CNN) are state-of-the-art models for many
image classification tasks. However, to recognize cancer subtypes
automatically, training a CNN on gigapixel resolution Whole Slide Tissue Images
(WSI) is currently computationally impossible. The differentiation of cancer
subtypes is based on cellular-level visual features observed on image patch
scale. Therefore, we argue that in this situation, training a patch-level
classifier on image patches will perform better than or similar to an
image-level classifier. The challenge becomes how to intelligently combine
patch-level classification results and model the fact that not all patches will
be discriminative. We propose to train a decision fusion model to aggregate
patch-level predictions given by patch-level CNNs, which to the best of our
knowledge has not been shown before. Furthermore, we formulate a novel
Expectation-Maximization (EM) based method that automatically locates
discriminative patches robustly by utilizing the spatial relationships of
patches. We apply our method to the classification of glioma and non-small-cell
lung carcinoma cases into subtypes. The classification accuracy of our method
is similar to the inter-observer agreement between pathologists. Although it is
impossible to train CNNs on WSIs, we experimentally demonstrate using a
comparable non-cancer dataset of smaller images that a patch-based CNN can
outperform an image-based CNN.

通过训练决策融合模型以及利用 EM 方法自动定位有效的 Patch，可以实现对肿瘤亚型的分类，其分类精度与病理学家之间的一致性相似，这种基于 Patch 的 CNN 可以比基于图像的 CNN 更好地处理图像分类任务。