An Ordinal Regression Framework for a Deep Learning Based Severity
  Assessment for Chest Radiographs
Patrick Wienholt Alexander Hermans Firas Khader Behrus Puladi Bastian Leibe Christiane Kuhl Sven Nebelung Daniel Truhn
This study investigates the application of ordinal regression methods for
categorizing disease severity in chest radiographs. We propose a framework that
divides the ordinal regression problem into three parts: a model, a target
function, and a classification function. Different encoding methods, including
one-hot, Gaussian, progress-bar, and our soft-progress-bar, are applied using
ResNet50 and ViT-B-16 deep learning models. We show that the choice of encoding
has a strong impact on performance and that the best encoding depends on the
chosen weighting of Cohen's kappa and also on the model architecture used. We
make our code publicly available on GitHub.