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

1. modified configs/ssformer-S.yaml
   • dataset set to your data path
   • test.checkpoint_save_path : path to your downloaded checkpoint
2. run python test.py configs/ssformer-S.yaml

Train

1. modified configs/train.yaml
   • model.pretrained_path : mit pre-trained checkpoint path
   • other : path to save your training checkpoint and log file
2. 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