Awesome
Stepwise Feature Fusion: Local Guides Global
This is the official implementation for Stepwise Feature Fusion: Local Guides Global
packages
- Please see requirements.txt
Dataset
- The dataset we used can be download from here
Checkpoints
- The checkpoint for ssformer-S can be downloaded from here
- The checkpoint for ssformer-L can be downloaded from here
Usage
Test
- modified
configs/ssformer-S.yaml
dataset
set to your data pathtest.checkpoint_save_path
: path to your downloaded checkpoint
- run
python test.py configs/ssformer-S.yaml
Train
- modified
configs/train.yaml
model.pretrained_path
: mit pre-trained checkpoint pathother
: path to save your training checkpoint and log file
- run
python train.py configs/train.yaml
Citation
Wang, J., Huang, Q., Tang, F., Meng, J., Su, J., Song, S. (2022).
Stepwise Feature Fusion: Local Guides Global.
In: Wang, L., Dou, Q., Fletcher, P.T., Speidel, S., Li, S. (eds)
Medical Image Computing and Computer Assisted Intervention – MICCAI 2022.
MICCAI 2022. Lecture Notes in Computer Science, vol 13433. Springer, Cham.
https://doi.org/10.1007/978-3-031-16437-8_11