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IM-Fuse: A Mamba-based Fusion Block for Brain Tumor Segmentation with Incomplete Modalities

[Our Paper] MICCAI 2025

IMFuse overview ✅ Tested at commit: 8359e49

Requirements

Code was tested using:

python==3.10.12
torch==2.7.1

How to run

Clone this repository, create a python env for the project and activate it. Then install all the dependencies with pip.

git clone git@github.com:AImageLab-zip/IM-Fuse.git
cd IMFuse
python -m venv imfuse_venv
source imfuse_venv/bin/activate
pip install -r requirements.txt

Preprocess data

First, run the preprocessing script preprocess.py with the following arguments:

python preprocess.py \
  --input-path <INPUT_PATH> \                  # Directory containing the unprocessed BRATS2023 dataset
  --output-path <OUTPUT_PATH>                  # Output directory

Training

Run the training script train_poly.py with the following arguments:

python train_poly.py \
  --datapath <PATH>/BRATS2023_Training_npy \   # Directory containing BRATS2023 .npy files
  --num_epochs 1000 \                          # Total number of training epochs
  --dataname BRATS2023 \                       # Dataset identifier
  --savepath <OUTPUT_PATH> \                   # Directory for saving checkpoints 
  --mamba_skip \                               # Using Mamba in the skip connections
  --interleaved_tokenization                   # Enable interleaved tokenization

Test

Run the test script test.py with the following arguments:

python test.py \
  --datapath <PATH>/BRATS2023_Training_npy \   # Directory containing BRATS2023 .npy files
  --dataname BRATS2023 \                       # Dataset identifier
  --savepath <OUTPUT_PATH> \                   # Directory for saving results
  --resume <RESUME_PATH> \                     # Path to the checkpoints 
  --mamba_skip \                               # Using Mamba in the skip connections
  --batch_size 2 \                             # Batch size
  --interleaved_tokenization                   # Enable interleaved tokenization