论文标题
对抗预测指导的多任务适应电子显微镜图像的语义分割
Adversarial-Prediction Guided Multi-task Adaptation for Semantic Segmentation of Electron Microscopy Images
论文作者
论文摘要
语义分割是电子显微镜（EM）图像分析的重要步骤。尽管监督模型取得了重大进展，但对劳动密集型像素的需求是一个主要限制。为了使事情进一步复杂化，由于域移动，监督的学习模型可能无法在新的数据集上概括。在这项研究中，我们介绍了一个对抗性预测指导的多任务网络，以了解训练有素的模型，以在新型未标记的目标域中使用。由于目标域上没有标签可用，因此我们不仅学习用于源域上的监督分割的编码表示，而且还学习目标数据的无监督重建。为了通过几何线索提高判别能力，我们进一步指导语义预测空间中多层对抗学习的表示。对公共基准的比较和消融研究表明，我们的方法的最先进的表现和有效性。
Semantic segmentation is an essential step for electron microscopy (EM) image analysis. Although supervised models have achieved significant progress, the need for labor intensive pixel-wise annotation is a major limitation. To complicate matters further, supervised learning models may not generalize well on a novel dataset due to domain shift. In this study, we introduce an adversarial-prediction guided multi-task network to learn the adaptation of a well-trained model for use on a novel unlabeled target domain. Since no label is available on target domain, we learn an encoding representation not only for the supervised segmentation on source domain but also for unsupervised reconstruction of the target data. To improve the discriminative ability with geometrical cues, we further guide the representation learning by multi-level adversarial learning in semantic prediction space. Comparisons and ablation study on public benchmark demonstrated state-of-the-art performance and effectiveness of our approach.
