计算机科学
对抗制
人工智能
乳腺癌
机器学习
生成对抗网络
生成语法
癌症
深度学习
医学
内科学
作者
Fan Zhang,Yingqi Zhang,Xiaoke Zhu,Xiaopan Chen,Haishun Du,Xinhong Zhang
标识
DOI:10.1016/j.cmpb.2022.107026
摘要
Background and Objective: Generative adversarial network (GAN) is able to learn from a set of training data and generate new data with the same characteristics as the training data. Based on the characteristics of GAN, this paper developed its capability as a tool of disease prognosis prediction, and proposed a prognostic model PregGAN based on conditional generative adversarial network (CGAN). Methods: The idea of PregGAN is to generate the prognosis prediction results based on the clinical data of patients. PregGAN added the clinical data as conditions to the training process. Conditions were used as the input to the generator along with noises. The generator synthesized new samples using the noises vectors and the conditions. In order to solve the mode collapse problem during PregGAN training, Wasserstein distance and gradient penalty strategy were used to make the training process more stable. Results: In the prognosis prediction experiments using the METABRIC breast cancer dataset, PregGAN achieved good results, with the average accurate (ACC) of 90.6% and the average AUC (area under curve) of 0.946. Conclusions: Experimental results show that PregGAN is a reliable prognosis predictive model for breast cancer. Due to the strong ability of probability distribution learning, PregGAN can also be used for the prognosis prediction of other diseases.
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