Evolutionary Neural Architecture Search for Retinal Vessel Segmentation

Abstract

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.

References in corpus (5)

• Attention U-Net: Learning Where to Look for the Pancreas
• Neural Architecture Search with Reinforcement Learning
• Instance Normalization: The Missing Ingredient for Fast Stylization
• DUNet: A deformable network for retinal vessel segmentation
• Scalable Neural Architecture Search for 3D Medical Image Segmentation

Cited by in corpus (1)

• A Survey on Evolutionary Neural Architecture Search