Evolutionary Neural Architecture Search for Retinal Vessel Segmentation

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.

[1]  Ramesh Raskar,et al.  Designing Neural Network Architectures using Reinforcement Learning , 2016, ICLR.

[2]  Tillman Weyde,et al.  M2U-Net: Effective and Efficient Retinal Vessel Segmentation for Real-World Applications , 2019, 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW).

[3]  Tuan D. Pham,et al.  DUNet: A deformable network for retinal vessel segmentation , 2018, Knowl. Based Syst..

[4]  Andrew Zisserman,et al.  Very Deep Convolutional Networks for Large-Scale Image Recognition , 2014, ICLR.

[5]  Kalyanmoy Deb,et al.  NSGA-Net: neural architecture search using multi-objective genetic algorithm , 2018, GECCO.

[6]  Vijay Vasudevan,et al.  Learning Transferable Architectures for Scalable Image Recognition , 2017, 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition.

[7]  Kilian Q. Weinberger,et al.  Densely Connected Convolutional Networks , 2016, 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[8]  Quoc V. Le,et al.  NAS-FPN: Learning Scalable Feature Pyramid Architecture for Object Detection , 2019, 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).

[9]  Shiyu Chang,et al.  AutoGAN: Neural Architecture Search for Generative Adversarial Networks , 2019, 2019 IEEE/CVF International Conference on Computer Vision (ICCV).

[10]  Zhun Fan,et al.  Automated blood vessel segmentation based on de-noising auto-encoder and neural network , 2016, 2016 International Conference on Machine Learning and Cybernetics (ICMLC).

[11]  Li Fei-Fei,et al.  Auto-DeepLab: Hierarchical Neural Architecture Search for Semantic Image Segmentation , 2019, 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).

[12]  Theodore Lim,et al.  SMASH: One-Shot Model Architecture Search through HyperNetworks , 2017, ICLR.

[13]  Bunyarit Uyyanonvara,et al.  An Ensemble Classification-Based Approach Applied to Retinal Blood Vessel Segmentation , 2012, IEEE Transactions on Biomedical Engineering.

[14]  Alan L. Yuille,et al.  Genetic CNN , 2017, 2017 IEEE International Conference on Computer Vision (ICCV).

[15]  Geoffrey E. Hinton,et al.  Rectified Linear Units Improve Restricted Boltzmann Machines , 2010, ICML.

[16]  Md Zahangir Alom,et al.  Recurrent residual U-Net for medical image segmentation , 2019, Journal of medical imaging.

[17]  Sergey Ioffe,et al.  Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift , 2015, ICML.

[18]  Xin Yang,et al.  Joint Segment-Level and Pixel-Wise Losses for Deep Learning Based Retinal Vessel Segmentation , 2018, IEEE Transactions on Biomedical Engineering.

[19]  Matthew B. Blaschko,et al.  A Discriminatively Trained Fully Connected Conditional Random Field Model for Blood Vessel Segmentation in Fundus Images , 2017, IEEE Transactions on Biomedical Engineering.

[20]  Loïc Le Folgoc,et al.  Attention U-Net: Learning Where to Look for the Pancreas , 2018, ArXiv.

[21]  Daguang Xu,et al.  Searching Learning Strategy with Reinforcement Learning for 3D Medical Image Segmentation , 2019, MICCAI.

[22]  Yujie Li,et al.  NAS-Unet: Neural Architecture Search for Medical Image Segmentation , 2019, IEEE Access.

[23]  Jimmy Ba,et al.  Adam: A Method for Stochastic Optimization , 2014, ICLR.

[24]  Masanori Suganuma,et al.  Evolution of Deep Convolutional Neural Networks Using Cartesian Genetic Programming , 2020, Evolutionary Computation.

[25]  Taesup Kim,et al.  Scalable Neural Architecture Search for 3D Medical Image Segmentation , 2019, MICCAI.

[26]  Ulas Bagci,et al.  Automatically Designing CNN Architectures for Medical Image Segmentation , 2018, MLMI@MICCAI.

[27]  Mark Sandler,et al.  MobileNetV2: Inverted Residuals and Linear Bottlenecks , 2018, 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition.

[28]  Jian Sun,et al.  Deep Residual Learning for Image Recognition , 2015, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[29]  Daguang Xu,et al.  V-NAS: Neural Architecture Search for Volumetric Medical Image Segmentation , 2019, 2019 International Conference on 3D Vision (3DV).

[30]  Andrea Vedaldi,et al.  Instance Normalization: The Missing Ingredient for Fast Stylization , 2016, ArXiv.

[31]  Kaiming He,et al.  Focal Loss for Dense Object Detection , 2017, 2017 IEEE International Conference on Computer Vision (ICCV).

[32]  Hans-Paul Schwefel,et al.  Evolution strategies – A comprehensive introduction , 2002, Natural Computing.

[33]  Geoffrey E. Hinton,et al.  Lookahead Optimizer: k steps forward, 1 step back , 2019, NeurIPS.

[34]  Hang Xu,et al.  Auto-FPN: Automatic Network Architecture Adaptation for Object Detection Beyond Classification , 2019, 2019 IEEE/CVF International Conference on Computer Vision (ICCV).

[35]  Quoc V. Le,et al.  Neural Architecture Search with Reinforcement Learning , 2016, ICLR.