Brats and EluNet

Brats-2021 Dataset Brats-2020 Dataset Brats-2019 Dataset Brats-2018 Dataset

Unet-and-his-Encoders

Unet++-and-his-Encoders

Unet+++-and-his-Encoders

pspnet-and-his-Encoders

ELUnet-and-his-Encoders

The official code for "Brats2021"and

The official code for "Brats2020" and

The official code for "Brats2019" and

The official code for "Brats2018".

Unet with ELU activision as Decoder and Strong cnn as Encoder Unet++ with ELU activision as Decoder and Strong cnn as Encoder Unet+++ with ELU activision as Decoder and Strong cnn as Encoder

Unet with ELU activision as Decoder and DenseNet 121 as Encoder Unet++ with ELU activision as Decoder and DenseNet 121 as Encoder Unet++ with ELU activision as Decoder and DenseNet 121 as Encoder

Unet++ with ELU activision as Decoder and MobileNetV1 as Encoder Unet++ with ELU activision as Decoder and MobileNetV2 as Encoder Unet++ with ELU activision as Decoder and MobileNetV3 Small as Encoder Unet++ with ELU activision as Decoder and MobileNetV3 Large as Encoder

Updates

Citation

@article{
  year={2023}
}

How to use

first download models and save them in same directory with IPYNB file as jupyter notebooks then Run nootbooks.

Model weights

Kaggle Drive Link:().

Training and Testing

1. Download the face Brats-2021 dataset from here. "Brats2020". "Brats2019". "Brats2018".

2. 2. Run the following code to install the Requirements.

   pip install -r requirements.txt

3. Run the below code to train the Unet++ with ELU activision as Decoder and... as Encoder with this dataset.

Unet++ with ELU activision as Decoder and Strong cnn as Encoder Unet++ with ELU activision as Decoder and DenseNet 121 as Encoder

6. Test trained model with this dataset in in IPYNB too.

Results

Performance comparision on Brats-segmentation dataset.

#2021 results #2020 results #2019 results