  Collecting large-scale medical datasets with fine-grained annotations is
time-consuming and requires experts. For this reason, weakly supervised
learning aims at optimising machine learning models using weaker forms of
annotations, such as scribbles, which are easier and faster to collect.
Unfortunately, training with weak labels is challenging and needs
regularisation. Herein, we introduce a novel self-supervised multi-scale
consistency loss, which, coupled with an attention mechanism, encourages the
segmentor to learn multi-scale relationships between objects and improves
performance. We show state-of-the-art performance on several medical and
non-medical datasets. The code used for the experiments is available at
https://vios-s.github.io/multiscale-pyag.
