mmFormer: Multimodal Medical Transformer for Incomplete Multimodal Learning of Brain Tumor Segmentation

Jul 25, 2022 1 min read

mmFormer: Multimodal Medical Transformer for Incomplete Multimodal Learning of Brain Tumor Segmentation

Paper

This is the implementation for the paper:

mmFormer: Multimodal Medical Transformer for Incomplete Multimodal Learning of Brain Tumor Segmentation

Accepted to MICCAI 2022

Usage.

Data Preparation
- Download the data from MICCAI 2018 BraTS Challenge.
- Put Training folder in ./data
- In ./data, preprocess the data by python preprocess.py
Train
- Train the model by
python -m torch.distributed.launch --nproc_per_node=4 --master_port 20003 train.py
Test
- inference on the test data by
python test
- To inference with missing modalities, please refer to line 201 in BraTS.py
  
  missing_modal_list.append(MISSING_MODAL)
  
  MISSING_MODAL is a list of missing modalities and each modality is denoted by a number.
  
  0: FLAIR, 1:T1CE, 2:T1, 3:T2

Reference

TransBTS

GitHub

View Github

Transformer Segmentation

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