论文标题
Syncgan：使用可学习的类特定先验来生成合成数据，以改善分类器的性能
SynCGAN: Using learnable class specific priors to generate synthetic data for improving classifier performance on cytological images
论文作者
论文摘要
医学图像分析最具挑战性的方面之一是缺乏大量注释数据。这使得深度学习算法由于缺乏输入空间的变化而难以表现出色。虽然生成的对抗网络已经在合成数据生成领域表现出了希望，但是如果没有精心设计的事先生成过程，则无法很好地执行。在提出的方法中，我们证明了自动生成的分割蒙版用作可学习的类特异性先验的使用，以指导有条件的gan来生成病情现实的样本来进行细胞学图像。我们已经观察到，使用称为“ Syncgan”的拟议管道增强数据可显着改善诸如Resnet-152，Densenet-161，Inception-V3之类的最先进的分类器的性能。
One of the most challenging aspects of medical image analysis is the lack of a high quantity of annotated data. This makes it difficult for deep learning algorithms to perform well due to a lack of variations in the input space. While generative adversarial networks have shown promise in the field of synthetic data generation, but without a carefully designed prior the generation procedure can not be performed well. In the proposed approach we have demonstrated the use of automatically generated segmentation masks as learnable class-specific priors to guide a conditional GAN for the generation of patho-realistic samples for cytology image. We have observed that augmentation of data using the proposed pipeline called "SynCGAN" improves the performance of state of the art classifiers such as ResNet-152, DenseNet-161, Inception-V3 significantly.
