Welcome to the official implementation of MAProtoNet (Multi-scale Attentive Prototypical Network).
MAProtoNet is an interpretable 3D MRI model for brain tumor classification, training on the public BraTS datasets. We improve the localization capability of the prototypical part networks for medicine imaging, by introducing the novel quadruplet attention layers as well as the multi-scale module.
Please check the following sections for more details:
| Repository Directory |
|---|
| Directory Structure |
| Environment Configurations |
| Datasets |
| Experiments Reproduction |
| Results |
| Acknowledgment |
| Citation |
If necessary, please check further details from our paper "MAProtoNet: A Multi-scale Attentive Interpretable Prototypical Part Network for 3D Magnetic Resonance Imaging Brain Tumor Classification", or contact us through email: s237857s@st.go.tuat.ac.jp
The directories of this repository are established as below:
- tumor_cls.py (main function for running our experiments)
- train.py (code for training and evaluation)
- run.sh (shell to run the experiments)
- models/ (code for our MAProtoNet model)
- utils/ (code for utility functions)
- data/ (code for BraTS datasets preparing)
- figures/ (images for this repository)
- requirements.txt (environment configurations)
- readme.md
We employ BraTS2018, BraTS2019, BraTS2020 datasets in our experiments. After applying for and downloading the datasets, for BraTS2018 dataset, please run data/get_mapping.py to generate name_mapping.csv before training.
Our experiments find that our MAProtoNet can achieve much better locolization capability with higher activation precision (AP) score, while maintaining similar balanced accuracy (BAC) and incremental deletion score (IDS) scores.
| Methods | BAC ↑ | AP ↑ | IDS ↓ |
|---|---|---|---|
| CNN | 85.5 | 10.7 | 13.8 |
| ProtoPNet | 84.3 | 11.8 | 24.2 |
| XProtoNet | 84.7 | 16.9 | 16.3 |
| MProtoNet | 85.8 | 81.2 | 6.2 |
| MAProtoNet (ours) | 86.7 | 85.8 | 6.2 |
This repository is established upon the source code of MProtoNet, from https://github.com/aywi/mprotonet developed by Yuanyuan Wei, Roger Tam and Xiaoying Tang. We appreciate their work.
If you found this repository useful, please consider citing:
@article{
binghua_maprotonet_2024,
author = {Binghua, Li and Jie, Mao and Zhe, Sun and Chao, Li and Qibin, Zhao and Toshihisa, Tanaka},
title = {{MAP}roto{N}et: A Multi-scale Attentive Interpretable Prototypical Part Network for 3{D} Magnetic Resonance Imaging Brain Tumor Classificati},
journal = {arXiv preprint arXiv: 2404.08917},
year = {2024},
month = {Apr},
url = {http://arxiv.org/abs/2404.08917},
publisher = {{arXiv}},
}