《Table 1Overview of the dataset used in this paper.》

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《基于Wasserstein GAN的新一代人工智能小样本数据增强方法——以生物领域癌症分期数据为例》

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In particular，when the number of synthetic samples generated by WGAN was 4000，the test accuracy of the real samples was70.97%（of the 31 original samples，22 were predicted correctly），the F-measure was 70.07%，and the G-mean was 68.39%.Table 2presents the DNN prediction results for all of the stages.All of the healthy samples in the control group were correctly predicted.Of the eight TNM stage I test samples，five samples were correctly predicted，two were predicted to be TNM stage II，and one was predicted to be stage III.Of the ten TNM stage II test samples，seven samples were predicted correctly；two were predicted to be healthy，indicating a risk of misdiagnosis；and one was predicted to be TNM stage III.Of the six TNM stage III real cases，only three were correctly predicted；the rest were predicted to be TNM stage I，for an accuracy of only 50%.This decreased the overall DNN model performance.Therefore，the number of original TNM stage III samples should be increased in a future study，in order to improve the specificity of the DNN model for TNM stage III.TNM stages I and II have similar clinical features and can be classified into a single category called‘‘early-stage cancer.”Thus，according to the results of the early-stage HCC（TNM stages I and II）identification，the accuracy of the proposed method reached 77.78%.This level of accuracy has great significance for the early identification and treatment of HCC，because a current study[69]has found that early treatment significantly increases the survival rate of patients with HCC.A recent study by Holzinger et al.[70]indicates that digital pathology will dramatically change medical workflows if pathologists are augmented by machine-learning methods.Thus，our integrated approach with accurate prediction holds potential to promote further research into the pathogenesis of HCC.