Pytorch-Segmentation-multi-models
Pytorch implementation for Semantic Segmentation with multi models for blood vessel segmentation in fundus images of DRIVE dataset.
Deeplabv3, Deeplabv3_plus, PSPNet, UNet, UNet_AutoEncoder, UNet_nested, R2AttUNet, AttentionUNet, RecurrentUNet, SEGNet, CENet, DsenseASPP, RefineNet, RDFNet
Data
Available at https://www.isi.uu.nl/Research/Databases/DRIVE/
Training
python train.py --model unet
You can modify --model to change models.
Reference:
AttentionR2Unet:
Recurrent Residual Convolutional Neural Network based on U-Net (R2U-Net) for Medical Image Segmentation https://arxiv.org/abs/1802.06955
AttentionUnet:
Attention U-Net: Learning Where to Look for the Pancreas https://arxiv.org/abs/1804.03999
CENet:
CE-Net: Context encoder network for 2D medical image segmentation https://arxiv.org/abs/1903.02740
DeepLabV3:
Rethinking Atrous Convolution for Semantic Image Segmentation https://arxiv.org/pdf/1706.05587.pdf
DeepLabV3_plus:
Encoder-Decoder with Atrous Separable Convolution for Semantic Image Segmentation https://arxiv.org/pdf/1802.02611.pdf
DenseASPP:
DenseASPP for Semantic Segmentation in Street Scenes http://openaccess.thecvf.com/content_cvpr_2018/papers/Yang_DenseASPP_for_Semantic_CVPR_2018_paper.pdf
PSPNet:
Pyramid Scene Parsing Network https://arxiv.org/abs/1612.01105
RDFNet:
RDFNet: RGB-D Multi-level Residual Feature Fusion for Indoor Semantic Segmentation
RecurrentUnet:
Recurrent Residual Convolutional Neural Network based on U-Net (R2U-Net) for Medical Image Segmentation https://arxiv.org/ftp/arxiv/papers/1802/1802.06955.pdf
RefineNet:
RefineNet: Multi-Path Refinement Networks for High-Resolution Semantic Segmentation https://arxiv.org/pdf/1611.06612.pdf
SegNet:
SegNet: A Deep Convolutional Encoder-Decoder Architecture for Image Segmentation https://arxiv.org/abs/1511.00561
U-Net:
Convolutional Networks for Biomedical Image Segmentation https://arxiv.org/abs/1505.04597
Unet_nested:
Unet++: A Nested U-Net Architecture for Medical Image Segmentation https://arxiv.org/pdf/1807.10165.pdf
Github:
https://github.com/Guzaiwang/CE-N