论文标题
进化神经建筑搜索视网膜骨骼分割
Evolutionary Neural Architecture Search for Retinal Vessel Segmentation
论文作者
论文摘要
准确的视网膜血管分割（RVS）对于帮助医生诊断眼科疾病和其他全身性疾病具有重要意义。手动设计有效的神经网络体系结构，以进行视网膜船只细分，需要大量的专业知识和大量的工作量。为了提高血管分割的性能并减少手动设计神经网络的工作量，我们提出了新的方法，该方法采用神经架构搜索（NAS）来优化用于视网膜血管分割的编码器架构。修改后的进化算法用于使用有限的计算资源来发展编码器框架的架构。通过所提出的方法获得的进化模型在三个数据集上的所有方法（即驱动器，凝视和chase_db1）上获得了最高的性能，但参数较少。此外，交叉训练的结果表明，进化的模型具有相当大的可伸缩性，这表明临床疾病诊断的潜力很大。
The accurate retinal vessel segmentation (RVS) is of great significance to assist doctors in the diagnosis of ophthalmology diseases and other systemic diseases. Manually designing a valid neural network architecture for retinal vessel segmentation requires high expertise and a large workload. In order to improve the performance of vessel segmentation and reduce the workload of manually designing neural network, we propose novel approach which applies neural architecture search (NAS) to optimize an encoder-decoder architecture for retinal vessel segmentation. A modified evolutionary algorithm is used to evolve the architectures of encoder-decoder framework with limited computing resources. The evolved model obtained by the proposed approach achieves top performance among all compared methods on the three datasets, namely DRIVE, STARE and CHASE_DB1, but with much fewer parameters. Moreover, the results of cross-training show that the evolved model is with considerable scalability, which indicates a great potential for clinical disease diagnosis.
