This research investigates using ordinal regression methods for categorizing disease severity in chest radiographs. The researchers 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 their ‘soft-progress-bar’, are applied using ResNet50 and ViT-B-16 deep learning models. The study finds that the choice of encoding has a significant impact on performance, and the best encoding depends on the chosen weighting of Cohen’s kappa and also on the model architecture used.
Publication date: 8 Feb 2024
Project Page: https://arxiv.org/abs/2402.05685v1
Paper: https://arxiv.org/pdf/2402.05685
