Learning Disease State from Noisy Ordinal Disease Progression Labels
Journal:
arXiv
Published Date:
Mar 13, 2025
Abstract
Learning from noisy ordinal labels is a key challenge in medical imaging. In
this work, we ask whether ordinal disease progression labels (better, worse, or
stable) can be used to learn a representation allowing to classify disease
state. For neovascular age-related macular degeneration (nAMD), we cast the
problem of modeling disease progression between medical visits as a
classification task with ordinal ranks. To enhance generalization, we tailor
our model to the problem setting by (1) independent image encoding, (2)
antisymmetric logit space equivariance, and (3) ordinal scale awareness. In
addition, we address label noise by learning an uncertainty estimate for loss
re-weighting. Our approach learns an interpretable disease representation
enabling strong few-shot performance for the related task of nAMD activity
classification from single images, despite being trained only on image pairs
with ordinal disease progression labels.
