论文标题
对抗性相互泄漏网络用于细胞图像分割
Adversarial Mutual Leakage Network for Cell Image Segmentation
论文作者
论文摘要
我们建议使用GAN和发电机和歧视器之间的信息泄漏提出三种分割方法。首先，我们提出了一个对抗性训练注意模块（ATA模块），该模块使用从鉴别器到发电机的注意机制来增强和泄漏歧视器中的重要信息。 ATA模块将重要信息从歧视器传输到生成器。其次，我们提出了一个自上而下的像素难度注意模块（自上而下的PDA模块），该模块泄漏了基于发电机中像素难度的注意力图的注意力图。发电机训练着专注于像素的难度，并且歧视器使用从发电机泄漏的难度信息进行分类。最后，我们提出了一个对抗性相互泄漏网络（AML-NET），该网络在发电机和鉴别器之间相互泄漏信息。通过使用另一个网络的信息，它比普通分割模型更有效地训练。已经在两个数据集上评估了三种提出的方法以进行细胞图像分割。实验结果表明，与常规方法相比，AML-NET的分割精度得到了很大提高。
We propose three segmentation methods using GAN and information leakage between generator and discriminator. First, we propose an Adversarial Training Attention Module (ATA-Module) that uses an attention mechanism from the discriminator to the generator to enhance and leak important information in the discriminator. ATA-Module transmits important information to the generator from the discriminator. Second, we propose a Top-Down Pixel-wise Difficulty Attention Module (Top-Down PDA-Module) that leaks an attention map based on pixel-wise difficulty in the generator to the discriminator. The generator trains to focus on pixel-wise difficulty, and the discriminator uses the difficulty information leaked from the generator for classification. Finally, we propose an Adversarial Mutual Leakage Network (AML-Net) that mutually leaks the information each other between the generator and the discriminator. By using the information of the other network, it is able to train more efficiently than ordinary segmentation models. Three proposed methods have been evaluated on two datasets for cell image segmentation. The experimental results show that the segmentation accuracy of AML-Net was much improved in comparison with conventional methods.
