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Unet-Segmentation-Pytorch-Nest-of-Unets

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    Malav Bateriwala authored and GitHub committed
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    Unet-Segmentation-Pytorch-Nest-of-Unets

    Implementation of different kinds of Unet Models for Image Segmentation

    1. UNet - U-Net: Convolutional Networks for Biomedical Image Segmentation https://arxiv.org/abs/1505.04597

    2. RCNN-UNet - Recurrent Residual Convolutional Neural Network based on U-Net (R2U-Net) for Medical Image Segmentation https://arxiv.org/abs/1802.06955

    3. Attention Unet - Attention U-Net: Learning Where to Look for the Pancreas https://arxiv.org/abs/1804.03999

    4. RCNN-Attention Unet - Attention R2U-Net : Just integration of two recent advanced works (R2U-Net + Attention U-Net) LeeJun Implementation - https://github.com/LeeJunHyun/Image_Segmentation.git

    5. Nested UNet - UNet++: A Nested U-Net Architecture for Medical Image Segmentation https://arxiv.org/abs/1807.10165

    With Layer Visualization

    1. Getting Started

    Clone the repo:

    git clone https://github.com/bigmb/Unet-Segmentation-Pytorch-Nest-of-Unets.git

    2. Requirements

    python>=3.6
    torch>=0.4.0
    torchvision
    torchsummary
    tensorboardx
    natsort
    numpy
    pillow
    scipy
    scikit-image
    sklearn

    Install all dependent libraries:

    pip install -r requirements.txt

    3. Types of Unet

    Unet unet1

    RCNN Unet r2unet

    Attention Unet att-unet

    Attention-RCNN Unet att-r2u

    Nested Unet

    nested

    4. Visualization

    To plot the loss , Visdom would be required. The code is already written, just uncomment the required part. Gradient flow can be used too. Taken from (https://discuss.pytorch.org/t/check-gradient-flow-in-network/15063/10)

    A model folder is created and all the data is stored inside that. Last layer will be saved in the model folder. If any particular layer is required , mention it in the line 361.

    Layer Visulization

    l2

    Filter Visulization

    filt1

    Input Image Visulization for checking

    a) Original Image

    in1

    b) CenterCrop Image

    in2

    5. Results

    Dice Score

    Dice