论文标题
内部：CNN中使用非成像信息的转向空间关注
INSIDE: Steering Spatial Attention with Non-Imaging Information in CNNs
论文作者
论文摘要
我们考虑将非成像信息集成到分割网络以提高性能的问题。诸如膜之类的调节层提供了以线性方式选择性扩增或抑制不同特征图的贡献的手段。但是，在卷积范式中很难学习空间依赖。在本文中，我们提出了一种机制，可以使用包含可区分参数函数（例如高斯）的特征注意机制在非成像信息下进行空间定位，然后再应用特征调制。我们将使用空间依赖性（内部）命名我们的方法实例调制。调节信息可能包括与时空或时空信息有关的任何因素，例如病变位置，大小和心脏周期阶段。我们的方法可以端到端训练，不需要其他监督。我们在两个数据集上评估了该方法：一个新的CLEVR-seg数据集，我们根据位置进行细分对象，而ACDC数据集则以心脏相和卷中的切片位置为条件。代码和CLEVR-SEG数据集可在https://github.com/jacenkow/inside上找到。
We consider the problem of integrating non-imaging information into segmentation networks to improve performance. Conditioning layers such as FiLM provide the means to selectively amplify or suppress the contribution of different feature maps in a linear fashion. However, spatial dependency is difficult to learn within a convolutional paradigm. In this paper, we propose a mechanism to allow for spatial localisation conditioned on non-imaging information, using a feature-wise attention mechanism comprising a differentiable parametrised function (e.g. Gaussian), prior to applying the feature-wise modulation. We name our method INstance modulation with SpatIal DEpendency (INSIDE). The conditioning information might comprise any factors that relate to spatial or spatio-temporal information such as lesion location, size, and cardiac cycle phase. Our method can be trained end-to-end and does not require additional supervision. We evaluate the method on two datasets: a new CLEVR-Seg dataset where we segment objects based on location, and the ACDC dataset conditioned on cardiac phase and slice location within the volume. Code and the CLEVR-Seg dataset are available at https://github.com/jacenkow/inside.
