  Features in images' backgrounds can spuriously correlate with the images'
classes, representing background bias. They can influence the classifier's
decisions, causing shortcut learning (Clever Hans effect). The phenomenon
generates deep neural networks (DNNs) that perform well on standard evaluation
datasets but generalize poorly to real-world data. Layer-wise Relevance
Propagation (LRP) explains DNNs' decisions. Here, we show that the optimization
of LRP heatmaps can minimize the background bias influence on deep classifiers,
hindering shortcut learning. By not increasing run-time computational cost, the
approach is light and fast. Furthermore, it applies to virtually any
classification architecture. After injecting synthetic bias in images'
backgrounds, we compared our approach (dubbed ISNet) to eight state-of-the-art
DNNs, quantitatively demonstrating its superior robustness to background bias.
Mixed datasets are common for COVID-19 and tuberculosis classification with
chest X-rays, fostering background bias. By focusing on the lungs, the ISNet
reduced shortcut learning. Thus, its generalization performance on external
(out-of-distribution) test databases significantly surpassed all implemented
benchmark models.
