Glioblastoma multiform (GBM) is a kind of head tumor with an extraordinarily complex treatment process. The survival period is typically 14-16 months, and the 2 year survival rate is approximately 26%-33%. The clinical treatment strategies for the pseudoprogression (PsP) and true tumor progression (TTP) of GBM are different, so accurately distinguishing these two conditions is particularly significant.As PsP and TTP of GBM are similar in shape and other characteristics, it is hard to distinguish these two forms with precision. In order to differentiate them accurately, this paper introduces a feature learning method based on a generative adversarial network: DC-Al GAN. GAN consists of two architectures: generator and discriminator. Alexnet is used as the discriminator in this work. Owing to the adversarial and competitive relationship between generator and discriminator, the latter extracts highly concise features during training. In DC-Al GAN, features are extracted from Alexnet in the final classification phase, and the highly nature of them contributes positively to the classification accuracy.The generator in DC-Al GAN is modified by the deep convolutional generative adversarial network (DCGAN) by adding three convolutional layers. This effectively generates higher resolution sample images. Feature fusion is used to combine high layer features with low layer features, allowing for the creation and use of more precise features for classification. The experimental results confirm that DC-Al GAN achieves high accuracy on GBM datasets for PsP and TTP image classification, which is superior to other state-of-the-art methods.
翻译:Glioblastoma 多种形体(GBM) 是一种具有超复杂的治疗过程的头型肿瘤(GBM) 。 生存期一般为14-16个月, 存活率为2年, 存活率约为26%- 33%。 GBM 的假进步( PsP) 和真正的肿瘤进化( TTP) 的临床治疗策略不同, 所以准确地区分这两个条件特别显著。 As PsP 和 TTP 在 GBM 的形状和其他特性上非常相似, 很难精确地区分这两种形式 。 为了准确区分这两种形式, 本文引入了一种基于基因化对立网络的特征: DC- Al GAN。 GAN 由两种结构组成: 发电机和导师。 Alexnet 的临床治疗策略在这项工作中被使用。 由于发电机和导师之间的对抗性和竞争关系, 后者在培训过程中具有非常简洁的特征。 在最后的分类阶段, 从Alexnet 中提取这些特征, 高度的分类有助于分类 。 DC GAN 的发电机在高层次的图像中, 将这种高等级数据与高分辨率进行修改。