Insufficient training data is a major bottleneck for most deep learning
practices, not least in medical imaging where data is difficult to collect and
publicly available datasets are scarce due to ethics and privacy. This work
investigates the use of synthetic images, created by generative adversarial
networks (GANs), as the only source of training data. We demonstrate that for
this application, it is of great importance to make use of multiple GANs to
improve the diversity of the generated data, i.e. to sufficiently cover the
data distribution. While a single GAN can generate seemingly diverse image
content, training on this data in most cases lead to severe over-fitting. We
test the impact of ensembled GANs on synthetic 2D data as well as common image
datasets (SVHN and CIFAR-10), and using both DCGANs and progressively growing
GANs. As a specific use case, we focus on synthesizing digital pathology
patches to provide anonymized training data.

本文研究了在医学影像方面采用生成对抗网络（GANs）合成图像作为深度学习的训练数据来替代难以获取的真实数据集的可行性，同时探讨使用多个 GANs 以提高生成数据的差异性在数码病理学方面的应用。

生成对抗网络集成用于合成训练数据生成

Ensembles of GANs for synthetic training data generation

There is a strong need for automated systems to improve diagnostic quality
and reduce the analysis time in histopathology image processing. Automated
detection and classification of pathological tissue characteristics with
computer-aided diagnostic systems are a critical step in the early diagnosis
and treatment of diseases. Once a pathology image is scanned by a microscope
and loaded onto a computer, it can be used for automated detection and
classification of diseases. In this study, the DenseNet-161 and ResNet-50
pre-trained CNN models have been used to classify digital histopathology
patches into the corresponding whole slide images via transfer learning
technique. The proposed pre-trained models were tested on grayscale and color
histopathology images. The DenseNet-161 pre-trained model achieved a
classification accuracy of 97.89% using grayscale images and the ResNet-50
model obtained the accuracy of 98.87% for color images. The proposed
pre-trained models outperform state-of-the-art methods in all performance
metrics to classify digital pathology patches into 24 categories.

利用 DenseNet-161 和 ResNet-50 预训练 CNN 模型，通过迁移学习的方法对数字组织病理学补丁进行分类，实验结果表明，这种方法可以自动检测和分类疾病，并在分类效果方面表现出色。