论文标题
自我诉讼
Region-of-interest guided Supervoxel Inpainting for Self-supervision
论文作者
论文摘要
事实证明，自我监督的学习在充分利用生物医学图像细分中的所有可用数据方面是无价的。一个特别简单有效的实现自学的机制是indpaining，这是根据图像的其余部分预测任意缺失区域的任务。在这项工作中，我们将重点放在图像上作为自我监督的代理任务，并提出了两种新颖的结构变化，以进一步增强深层神经网络的性能。我们通过使用基于Superveoxel的掩码而不是随机掩蔽来指导生成图像的过程，并通过重点关注要在主要任务中进行分割的区域，我们将其称为利益区域。我们假设这些增加迫使网络学习更适合主要任务的语义，并在两种应用上测试我们的假设：脑肿瘤和白质超强度分割。我们从经验上表明，我们所提出的方法始终优于两种监督的CNN，而没有任何自学意义，并且在大型和小型训练套件的大小上都可以使用常规的基于内部的自学方法。
Self-supervised learning has proven to be invaluable in making best use of all of the available data in biomedical image segmentation. One particularly simple and effective mechanism to achieve self-supervision is inpainting, the task of predicting arbitrary missing areas based on the rest of an image. In this work, we focus on image inpainting as the self-supervised proxy task, and propose two novel structural changes to further enhance the performance of a deep neural network. We guide the process of generating images to inpaint by using supervoxel-based masking instead of random masking, and also by focusing on the area to be segmented in the primary task, which we term as the region-of-interest. We postulate that these additions force the network to learn semantics that are more attuned to the primary task, and test our hypotheses on two applications: brain tumour and white matter hyperintensities segmentation. We empirically show that our proposed approach consistently outperforms both supervised CNNs, without any self-supervision, and conventional inpainting-based self-supervision methods on both large and small training set sizes.
