Retinal diseases can cause irreversible vision loss in both eyes if not
diagnosed and treated early. Since retinal diseases are so complicated, retinal
imaging is likely to show two or more abnormalities. Current deep learning
techniques for segmenting retinal images with many labels and attributes have
poor detection accuracy and generalisability. This paper presents a multipath
convolutional neural network for multifeature segmentation. The proposed
network is lightweight and spatially sensitive to information. A patch-based
implementation is used to extract local image features, and focal modulation
attention blocks are incorporated between the encoder and the decoder for
improved segmentation. Filter optimisation is used to prevent filter overlaps
and speed up model convergence. A combination of convolution operations and
group convolution operations is used to reduce computational costs. This is the
first robust and generalisable network capable of segmenting multiple features
of fundus images (including retinal vessels, microaneurysms, optic discs,
haemorrhages, hard exudates, and soft exudates). The results of our
experimental evaluation on more than ten publicly available datasets with
multiple features show that the proposed network outperforms recent networks
despite having a small number of learnable parameters.

本研究提出了一种用于多特征分割的多路径卷积神经网络，该网络轻量且对信息敏感。通过基于图像块的实现提取局部图像特征，并在编码器和解码器之间引入焦点调制注意力块以改善分割效果。使用滤波器优化来防止滤波器重叠并加速模型收敛。通过卷积操作和组卷积操作的组合来降低计算成本。此网络能够对多个特征的眼底图像进行分割，并在多个公开数据集上的实验评估中表现出色，尽管其可学习参数较少。