Generative adversarial networks (GAN) are widely used in medical image analysis tasks, such as medical image segmentation and synthesis. In these works, adversarial learning is directly applied to the original supervised segmentation (synthesis) networks. The usage of adversarial learning is effective in improving visual perception performance since adversarial learning works as realistic regularization for supervised generators. However, the quantitative performance often cannot improve as much as the qualitative performance, and it can even become worse in some cases. In this paper, we explore how we can take better advantage of adversarial learning in supervised segmentation (synthesis) models and propose an adversarial confidence learning framework to better model these problems. We analyze the roles of discriminator in the classic GANs and compare them with those in supervised adversarial systems. Based on this analysis, we propose adversarial confidence learning, i.e., besides the adversarial learning for emphasizing visual perception, we use the confidence information provided by the adversarial network to enhance the design of supervised segmentation (synthesis) network. In particular, we propose using a fully convolutional adversarial network for confidence learning to provide voxel-wise and region-wise confidence information for the segmentation (synthesis) network. With these settings, we propose a difficulty-aware attention mechanism to properly handle hard samples or regions by taking structural information into consideration so that we can better deal with the irregular distribution of medical data. Furthermore, we investigate the loss functions of various GANs and propose using the binary cross entropy loss to train the proposed adversarial system so that we can retain the unlimited modeling capacity of the discriminator. Experimental results on clinical and challenge datasets show that our proposed network can achieve state-of-the-art segmentation (synthesis) accuracy. Further analysis also indicates that adversarial confidence learning can both improve the visual perception performance and the quantitative performance.

中文翻译：

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医学图像分割和合成的对抗置信度学习


生成对抗网络（GAN）广泛应用于医学图像分析任务，例如医学图像分割和合成。在这些作品中，对抗性学习直接应用于原始的监督分割（合成）网络。对抗性学习的使用可以有效地提高视觉感知性能，因为对抗性学习可以作为监督生成器的现实正则化。然而，定量性能往往无法像定性性能那样提高，在某些情况下甚至会变得更糟。在本文中，我们探讨了如何在监督分割（合成）模型中更好地利用对抗性学习，并提出一种对抗性置信学习框架来更好地建模这些问题。我们分析了判别器在经典 GAN 中的作用，并将其与监督对抗系统中的作用进行比较。基于此分析，我们提出了对抗性置信学习，即除了强调视觉感知的对抗性学习之外，我们利用对抗网络提供的置信信息来增强监督分割（合成）网络的设计。特别是，我们建议使用全卷积对抗网络进行置信度学习，为分割（合成）网络提供体素和区域置信信息。通过这些设置，我们提出了一种困难感知注意机制，通过考虑结构信息来正确处理硬样本或区域，以便我们能够更好地处理医疗数据的不规则分布。 此外，我们研究了各种 GAN 的损失函数，并建议使用二元交叉熵损失来训练所提出的对抗系统，以便我们可以保留判别器的无限建模能力。临床和挑战数据集的实验结果表明，我们提出的网络可以实现最先进的分割（合成）准确性。进一步的分析还表明，对抗性置信学习既可以提高视觉感知性能，也可以提高定量性能。