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brain-tumor-segmentation-framework-with-deep-nuanced-reasoning-and-swin-t's Introduction

Brain-Tumor-Segmentation-Framework-with-Deep-Nuanced-Reasoning-and-Swin-T

Related warehouses

模型 路径
U-net https://github.com/bubbliiiing/unet-pytorch
Unet 3+ https://github.com/ZJUGiveLab/UNet-Version
swin transformer https://github.com/SwinTransformer/Swin-Transformer-Semantic-Segmentation

Required environment

Install the requirements.txt dependencies

pip install -r requirements.txt

Training steps

Dataset preparation

Download dataset

https://www.kaggle.com/datasets/awsaf49/brats20-dataset-training-validation

https://www.kaggle.com/datasets/dschettler8845/brats-2021-task1

Dataset preprocessing

Running the data_progress.py

python data_progress.py

Model training

Running the train.py

CUDA_VISIBLE_DEVICES=0,1,2,3,4,5,6,7 python3 -m torch.distributed.launch --nproc_per_node 8 --master_port 20003 train.py

Citing Brain-Tumor-Segmentation-Framework-with-Deep-Nuanced-Reasoning-and-Swin-T.

This will be presented after the paper is published.

brain-tumor-segmentation-framework-with-deep-nuanced-reasoning-and-swin-t's People

Contributors

curbuni avatar

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