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.
@inproceedings{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}, booktitle={International Conference on Medical Image Computing and Computer-Assisted Intervention}, pages={173--182}, year={2023}, organization={Springer} }