Informed machine learning for cardiomegaly detection in chest X-rays: a comparative study

Recently, computer-aided disease detection from chest radiographs made considerable progress by using convolutional neural networks but issues like insufficient data quality or data availability remain. Informed machine learning (IML) combines domain knowledge and data-driven approaches and has been shown to improve results in many applications. However, there is limited research comparing and combining multiple IML approaches. This paper tackles this issue by implementing, combining, and evaluating three IML approaches for cardiomegaly detection. We find that curriculum learning and cropping images to regions of interest can improve prediction performance. With these results, we provide a reference for both implementing and evaluating multiple IML approaches as well as demonstrating methods to combine IML approaches.