AME-CAM: Attentive Multiple-Exit CAM for Weakly Supervised Segmentation on MRI Brain Tumor (AME-CAM) [MICCAI 23']
Official code implementation for the AME-CAM paper accepted by MICCAI 2023.
RSNA-ASNR-MICCAI Brain Tumor Segmentation (BraTS) Challenge 2021
Download the official BraTS 2021 Dataset Task 1.
Split the official training set into training and validation with the ratio 9:1. (The case id for training and validation set are shown in dataset.txt.)
Preprocess the dataset from 3D volume data into 2D slide with the following script.
cd ./src/
python3 gen_dataset.py -m t1 -d training/validate
Folder Structures for Dataset
DATASET_NAME
|-- flair
| |-- training
| | |-- normal
| | | |-- NORMAL_1.png
| | | |-- ...
| | |-- seg
| | | |-- TUMOR_1.png
| | | |-- ...
| | |-- tumor
| | | |-- TUMOR_1.jpg
| | | |-- ...
| |-- validate
| | |-- normal
| | | |-- NORMAL_1.png
| | | |-- ...
| | |-- seg
| | | |-- TUMOR_1.png
| | | |-- ...
| | |-- tumor
| | | |-- TUMOR_1.jpg
| | | |-- ...
|-- t1
|-- t1ce
|-- t2
cd ./src/encoder_phase/
python3 pretrain_clnet.py -m t1 --model_type Res18
cd ./src/encoder_phase/
python3 train_cnet.py -b 256 -m t1 --encoder_pretrained_path SimCLR/Res18_t1_ep100_b512
python3 test_cnet.py -m t1 --pretrained_path Res18_t1_ep10_b256.ME
cd ./src/attention_aggregation_network/
python3 train_cnet.py -b 256 -m t1 --encoder_pretrained_path Res18_t1_ep10_b256.ME
python3 test_cnet.py -m t1 --pretrained_path Res18_t1_ep10_b256.AME-CAM
cd ./src/AME-CAM_inference/
python3 main.py --pretrained_path Res18_t1_ep10_b256.AME-CAM -m t1
If you use the code or results in your research, please use the following BibTeX entry.
@article{chen2023ame,
title={AME-CAM: Attentive Multiple-Exit CAM for Weakly Supervised Segmentation on MRI Brain Tumor},
author={Chen, Yu-Jen and Hu, Xinrong and Shi, Yiyu and Ho, Tsung-Yi},
journal={arXiv preprint arXiv:2306.14505},
year={2023}
}