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.

翻译：由于极品级不平衡和体积差异巨大,将硬体外表的硬体外观图像自动分割成彩色基金图象是一项挑战性任务。 本文旨在解决这些问题, 并提议建立一个双部门网络, 包括双重抽样, 模版 Dice 损失。 它由两个分支组成: 大型硬体外观偏差学习分支和小体外观偏差学习分支。 两者分别负责各自的职责。 此外, 我们提议对培训进行双重抽样模拟 Dice 损失, 以便我们提议的双部门网络能够将硬体外观分解成不同大小。 详细来说, 我们使用一个统一的采样器, 从 Dice 损失计算所预测的分解面面面面面面面面面面部取样。 这导致这个分支自然偏向于大硬体外观, 因为 Dice 损失对错误识别大型硬体外观值比小。 第二分支, 我们用一个重新平衡的采样器, 过度标定硬体外观的硬面面面面面面面面面面的硬体背景面图, 使得我们较容易地计算成本。