Fully convolutional neural networks (F-CNNs) have set the state-of-the-art in
image segmentation for a plethora of applications. Architectural innovations
within F-CNNs have mainly focused on improving spatial encoding or network
connectivity to aid gradient flow. In this paper, we explore an alternate
direction of recalibrating the feature maps adaptively, to boost meaningful
features, while suppressing weak ones. We draw inspiration from the recently
proposed squeeze & excitation (SE) module for channel recalibration of feature
maps for image classification. Towards this end, we introduce three variants of
SE modules for image segmentation, (i) squeezing spatially and exciting
channel-wise (cSE), (ii) squeezing channel-wise and exciting spatially (sSE)
and (iii) concurrent spatial and channel squeeze & excitation (scSE). We
effectively incorporate these SE modules within three different
state-of-the-art F-CNNs (DenseNet, SD-Net, U-Net) and observe consistent
improvement of performance across all architectures, while minimally effecting
model complexity. Evaluations are performed on two challenging applications:
whole brain segmentation on MRI scans (Multi-Atlas Labelling Challenge Dataset)
and organ segmentation on whole body contrast enhanced CT scans (Visceral
Dataset).

本文提出使用 Squeeze & Excitation 模块，分别从空间和通道两个角度分为三个变体，提高卷积神经网络的特征表示能力，在脑部和器官分割上取得了良好的效果。