  As current computing capabilities increase, modern machine learning and
computer vision system tend to increase in complexity, mostly by means of
larger models and advanced optimization strategies. Although often neglected,
in many problems there is also much to be gained by considering potential
improvements in understanding and better leveraging already-available training
data, including annotations. This so-called data-centric approach can lead to
substantial performance increases, sometimes beyond what can be achieved by
larger models. In this paper we adopt such an approach for the task of
justifiable glaucoma screening from retinal images. In particular, we focus on
how to combine information from multiple annotators of different skills into a
tailored label smoothing scheme that allows us to better employ a large
collection of fundus images, instead of discarding samples suffering from
inter-rater variability. Internal validation results indicate that our bespoke
label smoothing approach surpasses the performance of a standard resnet50 model
and also the same model trained with conventional label smoothing techniques,
in particular for the multi-label scenario of predicting clinical reasons of
glaucoma likelihood in a highly imbalanced screening context. Our code is made
available at github.com/agaldran/justraigs .
