https://github.com/920232796/nestedformer

NestedFormer: Nested Modality-Aware Transformer for Brain Tumor Segmentation (MICCAI 2022)
https://github.com/920232796/nestedformer

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NestedFormer: Nested Modality-Aware Transformer for Brain Tumor Segmentation (MICCAI 2022)

• Host: GitHub
• URL: https://github.com/920232796/nestedformer
• Owner: 920232796
• License: apache-2.0
• Created: 2022-06-19T05:59:18.000Z (over 2 years ago)
• Default Branch: main
• Last Pushed: 2022-09-03T14:31:51.000Z (about 2 years ago)
• Last Synced: 2024-10-31T17:44:33.289Z (13 days ago)
• Language: Python
• Size: 35.2 KB
• Stars: 39
• Watchers: 2
• Forks: 4
• Open Issues: 15
• Metadata Files:
  • Readme: README.md
  • License: LICENSE

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README

        
# NestedFormer

NestedFormer (MICCAI2022) is a multimodal segmentation model on 3D medical images. The features of different modalities are fused through tri-oriented self-attention and cross-attention. We also improve the Poolfromer structure (CVPR2022) as an efficient encoder.

Read our paper https://arxiv.org/abs/2208.14876 on ArXiv for a formal introduction.

## Getting Started

### Setup

```commandline

pip install monai

pip install tqdm

pip install tensorboardX

```

### Download data

Please download the brats2020 datasets. Of course, switching to other datasets is ok.

### Run 

``` commandline

python main.py --distributed  --logdir=log_train_nestedformer --fold=0 --json_list=./brats2020_datajson.json --max_epochs=1000 --lrschedule=warmup_cosine --val_every=10 --data_dir=/data/MICCAI_BraTS2020_TrainingData/  --out_channels=3 --batch_size=1 --infer_overlap=0.5

```

--data_dir is the location of the data.

## Train your own dataset

The data processing code is in utils/data_utils.py. You can modify this code for your own dataset.

## Acknowledgment

Our implementation is mainly based on the following codebases. We gratefully thank the authors for their wonderful works.

pytorch, monai, [monai-research-contributions](https://github.com/Project-MONAI/research-contributions)