IEEE Trans Neural Netw Learn Syst. 2023 Nov;34(11):9375-9388. doi: 10.1109/TNNLS.2022.3159394. Epub 2023 Oct 27.
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 hard attention to the learned feature maps at different convolutional layers. The network also allows rectifying the predictions in an iterative fashion during the test time. We show that our proposed 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.
大量可用的大型临床和实验数据集的增加促进了生物医学图像分析领域的大量重要贡献。图像分割对于任何定量分析都是至关重要的，尤其受到关注。最近的硬件进步使得深度学习方法取得了成功。然而，尽管深度学习模型是在大型数据集上进行训练的，但现有方法并没有有效地利用来自不同学习阶段的信息。在这项工作中，我们利用每个训练阶段的信息来修剪后续阶段的预测图。我们提出了一种称为反馈注意网络（FANet）的新架构，它将前一阶段的掩模与当前训练阶段的特征图统一起来。然后，前一阶段的掩模用于为不同卷积层的学习特征图提供硬注意。该网络还允许在测试时以迭代的方式修正预测。我们表明，我们提出的反馈注意模型在七个公开可用的生物医学成像数据集上的大多数分割指标上都提供了显著的改进，证明了 FANet 的有效性。源代码可在 https://github.com/nikhilroxtomar/FANet 上获得。
