Fully Convolutional Networks have been achieving remarkable results in image
semantic segmentation, while being efficient. Such efficiency results from the
capability of segmenting several voxels in a single forward pass. So, there is
a direct spatial correspondence between a unit in a feature map and the voxel
in the same location. In a convolutional layer, the kernel spans over all
channels and extracts information from them. We observe that linear
recombination of feature maps by increasing the number of channels followed by
compression may enhance their discriminative power. Moreover, not all feature
maps have the same relevance for the classes being predicted. In order to learn
the inter-channel relationships and recalibrate the channels to suppress the
less relevant ones, Squeeze and Excitation blocks were proposed in the context
of image classification with Convolutional Neural Networks. However, this is
not well adapted for segmentation with Fully Convolutional Networks since they
segment several objects simultaneously, hence a feature map may contain
relevant information only in some locations. In this paper, we propose
recombination of features and a spatially adaptive recalibration block that is
adapted for semantic segmentation with Fully Convolutional Networks - the SegSE
block. Feature maps are recalibrated by considering the cross-channel
information together with spatial relevance. Experimental results indicate that
Recombination and Recalibration improve the results of a competitive baseline,
and generalize across three different problems: brain tumor segmentation,
stroke penumbra estimation, and ischemic stroke lesion outcome prediction. The
obtained results are competitive or outperform the state of the art in the
three applications.

本文提出了一种针对 Fully Convolutional Networks 进行语义分割的特征重组和空间自适应重新校准块 - SegSE 块。通过考虑跨通道信息和空间相关性来重新校准特征图，实验证明，重组和重新校准可以提高基线结果，并推广到三个不同问题：脑肿瘤分割，中风暗区估计和缺血性中风病变结局预测，其结果具有竞争力或优于现有技术水平。