The increase of available large clinical and experimental datasets has
contributed to a substantial amount of important contributions in the area of
biomedical image analysis. Image segmentation, which is crucial for any
quantitative analysis, has especially attracted attention. Recent hardware
advancement has led to the success of deep learning approaches. However,
although deep learning models are being trained on large datasets, existing
methods do not use the information from different learning epochs effectively.
In this work, we leverage the information of each training epoch to prune the
prediction maps of the subsequent epochs. We propose a novel architecture
called feedback attention network (FANet) that unifies the previous epoch mask
with the feature map of the current training epoch. The previous epoch mask is
then used to provide a hard attention to the learned feature maps at different
convolutional layers. The network also allows to rectify the predictions in an
iterative fashion during the test time. We show that our proposed
\textit{feedback attention} model provides a substantial improvement on most
segmentation metrics tested on seven publicly available biomedical imaging
datasets demonstrating the effectiveness of FANet. The source code is available
at https://github.com/nikhilroxtomar/FANet.

在生物医学图像分析领域，我们提出了一种称为反馈关注网络（FANet）的新型架构，利用每个训练时期的信息来修剪随后的预测地图，该网络在不同的卷积层上允许学习特征图的 “硬关注”，通过七个公开的生物医学成像数据集测试，显示出较大的平均分割指标提高。