  Automated segmentation of hard exudates in colour fundus images is a
challenge task due to issues of extreme class imbalance and enormous size
variation. This paper aims to tackle these issues and proposes a dual-branch
network with dual-sampling modulated Dice loss. It consists of two branches:
large hard exudate biased learning branch and small hard exudate biased
learning branch. Both of them are responsible for their own duty separately.
Furthermore, we propose a dual-sampling modulated Dice loss for the training
such that our proposed dual-branch network is able to segment hard exudates in
different sizes. In detail, for the first branch, we use a uniform sampler to
sample pixels from predicted segmentation mask for Dice loss calculation, which
leads to this branch naturally be biased in favour of large hard exudates as
Dice loss generates larger cost on misidentification of large hard exudates
than small hard exudates. For the second branch, we use a re-balanced sampler
to oversample hard exudate pixels and undersample background pixels for loss
calculation. In this way, cost on misidentification of small hard exudates is
enlarged, which enforces the parameters in the second branch fit small hard
exudates well. Considering that large hard exudates are much easier to be
correctly identified than small hard exudates, we propose an easy-to-difficult
learning strategy by adaptively modulating the losses of two branches. We
evaluate our proposed method on two public datasets and results demonstrate
that ours achieves state-of-the-art performances.
