MProtoNet

This repository contains the official implementation of MProtoNet from the paper "MProtoNet: A Case-Based Interpretable Model for Brain Tumor Classification with 3D Multi-parametric Magnetic Resonance Imaging" (accepted at MIDL 2023) by Yuanyuan Wei, Roger Tam, and Xiaoying Tang.

Talk: https://www.youtube.com/watch?v=DtfXwrliVQg

• Directory Structure
• Dataset
• Environment Configuration
• Experiments
• Results
• Acknowledgment
• Citation
• To-do List

Directory Structure

• data/: Dataset
• env_config/: Environment configuration
• results/: Results
• src/: Source code
  • tumor_cls.py: Major code for running the experiments

Dataset

• MICCAI Brain Tumor Segmentation (BraTS) Challenge 2020 (BraTS_2020)

Please follow the instructions on the website above to request the BraTS_2020 dataset (it may take 1~4 days) and download MICCAI_BraTS2020_TrainingData.zip to the data/ folder. Then unzip the dataset:

unzip ./data/MICCAI_BraTS2020_TrainingData.zip -d ./data/BraTS_2020

Environment Configuration

Prerequisites: Python (>=3.8), NumPy, SciPy, scikit-learn, Matplotlib, Jupyter, PyTorch, Captum, OpenCV, TorchIO.

If you are using Miniconda/Anaconda, you can run following commands (Linux & Windows) to create a new conda environment called mprotonet with all the required packages (they can take up about 10 GB of disk space).

conda env create -f ./env_config/environment.yml -n mprotonet -y
# Or if you want to install the latest versions of the packages (with Python 3.11 and CUDA 11):
# conda env create -f ./env_config/environment_latest.yml -n mprotonet -y
conda activate mprotonet
conda clean -a -y
conda env list

Experiments

The following experiments can consume about 12~18 GB of GPU memory.

cd ./src
# CNN (with GradCAM)
python ./tumor_cls.py -m CNN3D -n 100 -p "{'batch_size': [32], 'lr': [0.001], 'wd': [0.01], 'features': ['resnet152_ri'], 'n_layers': [6]}" --save-model 1
# ProtoPNet
python ./tumor_cls.py -m MProtoNet3D_pm1 -n 100 -p "{'batch_size': [32], 'lr': [0.001], 'wd': [0.01], 'features': ['resnet152_ri'], 'n_layers': [6], 'prototype_shape': [(30, 128, 1, 1, 1)], 'f_dist': ['cos'], 'topk_p': [1], 'coefs': [{'cls': 1, 'clst': 0.8, 'sep': -0.08, 'L1': 0.01, 'map': 0, 'OC': 0}]}" --save-model 1
# XProtoNet
python ./tumor_cls.py -m MProtoNet3D_pm2 -n 100 -p "{'batch_size': [32], 'lr': [0.001], 'wd': [0.01], 'features': ['resnet152_ri'], 'n_layers': [6], 'prototype_shape': [(30, 128, 1, 1, 1)], 'f_dist': ['cos'], 'topk_p': [1], 'coefs': [{'cls': 1, 'clst': 0.8, 'sep': -0.08, 'L1': 0.01, 'map': 0.5, 'OC': 0}]}" --save-model 1
# MProtoNet A
python ./tumor_cls.py -m MProtoNet3D_pm3 -n 100 -p "{'batch_size': [32], 'lr': [0.001], 'wd': [0.01], 'features': ['resnet152_ri'], 'n_layers': [6], 'prototype_shape': [(30, 128, 1, 1, 1)], 'f_dist': ['cos'], 'topk_p': [1], 'coefs': [{'cls': 1, 'clst': 0.8, 'sep': -0.08, 'L1': 0.01, 'map': 0.5, 'OC': 0}]}" --save-model 1
# MProtoNet B
python ./tumor_cls.py -m MProtoNet3D_pm4 -n 100 -p "{'batch_size': [32], 'lr': [0.001], 'wd': [0.01], 'features': ['resnet152_ri'], 'n_layers': [6], 'prototype_shape': [(30, 128, 1, 1, 1)], 'f_dist': ['cos'], 'topk_p': [1], 'coefs': [{'cls': 1, 'clst': 0.8, 'sep': -0.08, 'L1': 0.01, 'map': 0.5, 'OC': 0.05}]}" --save-model 1
# MProtoNet C
python ./tumor_cls.py -m MProtoNet3D_pm5 -n 100 -p "{'batch_size': [32], 'lr': [0.001], 'wd': [0.01], 'features': ['resnet152_ri'], 'n_layers': [6], 'prototype_shape': [(30, 128, 1, 1, 1)], 'f_dist': ['cos'], 'topk_p': [1], 'coefs': [{'cls': 1, 'clst': 0.8, 'sep': -0.08, 'L1': 0.01, 'map': 0.5, 'OC': 0.05}]}" --save-model 1

Results

Acknowledgment

This repository contains modified source code from cfchen-duke/ProtoPNet (MIT License) by Chaofan Chen, Oscar Li, Chaofan Tao, Alina Jade Barnett, and Cynthia Rudin.

Citation

Yuanyuan Wei, Roger Tam, and Xiaoying Tang. MProtoNet: a case-based interpretable model for brain tumor classification with 3D multi-parametric magnetic resonance imaging. In Medical Imaging with Deep Learning, Nashville, United States, July 2023.

@inproceedings{wei2023mprotonet,
  title = {{{MProtoNet}}: A Case-Based Interpretable Model for Brain Tumor Classification with {{3D}} Multi-Parametric Magnetic Resonance Imaging},
  shorttitle = {{{MProtoNet}}},
  booktitle = {Medical {{Imaging}} with {{Deep Learning}}},
  author = {Wei, Yuanyuan and Tam, Roger and Tang, Xiaoying},
  year = {2023},
  month = jul,
  address = {{Nashville, United States}},
  url = {https://openreview.net/forum?id=6Wbj3QCo4U4}
}

To-do List

• Dataset
• Environment configuration
• Upload source code
• Experiments
• Results