Image background features can constitute background bias (spurious
correlations) and impact deep classifiers decisions, causing shortcut learning
(Clever Hans effect) and reducing the generalization skill on real-world data.
The concept of optimizing Layer-wise Relevance Propagation (LRP) heatmaps, to
improve classifier behavior, was recently introduced by a neural network
architecture named ISNet. It minimizes background relevance in LRP maps, to
mitigate the influence of image background features on deep classifiers
decisions, hindering shortcut learning and improving generalization. For each
training image, the original ISNet produces one heatmap per possible class in
the classification task, hence, its training time scales linearly with the
number of classes. Here, we introduce reformulated architectures that allow the
training time to become independent from this number, rendering the
optimization process much faster. We challenged the enhanced models utilizing
the MNIST dataset with synthetic background bias, and COVID-19 detection in
chest X-rays, an application that is prone to shortcut learning due to
background bias. The trained models minimized background attention and hindered
shortcut learning, while retaining high accuracy. Considering external
(out-of-distribution) test datasets, they consistently proved more accurate
than multiple state-of-the-art deep neural network architectures, including a
dedicated image semantic segmenter followed by a classifier. The architectures
presented here represent a potentially massive improvement in training speed
over the original ISNet, thus introducing LRP optimization into a gamut of
applications that could not be feasibly handled by the original model.

利用 ISNet 架构及 Layer-wise Relevance Propagation (LRP) 热图优化，我们提出重新构建的架构，通过最小化背景相关性，防止深度分类器决策中的背景特征影响，从而减少捷径学习并提高泛化能力。在 MNIST 数据集和 COVID-19 胸部 X 射线检测中，我们的模型在最小化背景影响和阻止捷径学习的同时，保持了高准确性，相较于其他深度神经网络架构更具准确性，这表明这些架构在训练速度上是对 ISNet 的巨大改进。