  Deep neural networks have increasingly been used as an auxiliary tool in
healthcare applications, due to their ability to improve performance of several
diagnosis tasks. However, these methods are not widely adopted in clinical
settings due to the practical limitations in the reliability, generalizability,
and interpretability of deep learning based systems. As a result, methods have
been developed that impose additional constraints during network training to
gain more control as well as improve interpretabilty, facilitating their
acceptance in healthcare community. In this work, we investigate the benefit of
using Orthogonal Spheres (OS) constraint for classification of COVID-19 cases
from chest X-ray images. The OS constraint can be written as a simple
orthonormality term which is used in conjunction with the standard
cross-entropy loss during classification network training. Previous studies
have demonstrated significant benefits in applying such constraints to deep
learning models. Our findings corroborate these observations, indicating that
the orthonormality loss function effectively produces improved semantic
localization via GradCAM visualizations, enhanced classification performance,
and reduced model calibration error. Our approach achieves an improvement in
accuracy of 1.6% and 4.8% for two- and three-class classification,
respectively; similar results are found for models with data augmentation
applied. In addition to these findings, our work also presents a new
application of the OS regularizer in healthcare, increasing the post-hoc
interpretability and performance of deep learning models for COVID-19
classification to facilitate adoption of these methods in clinical settings. We
also identify the limitations of our strategy that can be explored for further
research in future.
