Abstract
In ordinal classification, misclassifying neighboring ranks is common, yet
the consequences of these errors are not the same. For example, misclassifying
benign tumor categories is less consequential, compared to an error at the
pre-cancerous to cancerous threshold, which could profoundly influence
treatment choices. Despite this, existing ordinal classification methods do not
account for the varying importance of these margins, treating all neighboring
classes as equally significant. To address this limitation, we propose CLOC, a
new margin-based contrastive learning method for ordinal classification that
learns an ordered representation based on the optimization of multiple margins
with a novel multi-margin n-pair loss (MMNP). CLOC enables flexible decision
boundaries across key adjacent categories, facilitating smooth transitions
between classes and reducing the risk of overfitting to biases present in the
training data. We provide empirical discussion regarding the properties of MMNP
and show experimental results on five real-world image datasets (Adience,
Historical Colour Image Dating, Knee Osteoarthritis, Indian Diabetic
Retinopathy Image, and Breast Carcinoma Subtyping) and one synthetic dataset
simulating clinical decision bias. Our results demonstrate that CLOC
outperforms existing ordinal classification methods and show the
interpretability and controllability of CLOC in learning meaningful, ordered
representations that align with clinical and practical needs.