This repository contains our solution "Iterative Semi-Supervised Learning for Abdominal Organs and Tumor Segmentation" for the FLARE23 challenge, based on nnU-Netv2.
Our approach is based on interative SSL, which employs a multi-stage pseudo-labeling method to tackle the issue of partial labels for organs and tumors in the FLARE23 dataset. For more details, see the pipeline diagram below:
To set up the environment, follow these steps:
conda create -n FLARE23
conda activate FLARE23
Then make sure to install PyTorch 2 compatible with your CUDA version.
pip install -e .
Our training process is totally depending on nnunetV2. Thus, you can find all the details in nnunet
Download the checkpoints and pseudo labels from BaiduNetDisk. Code：1111 Or you can directly use our generated pseudo labels.
python ensemble.py
The 'ensemble.py' is in the process directory. You SHOULD modify your defined paths of pseudo labels first!
- Place your input images in the
./inputsdirectory. - Run the prediction script:
sh predict.sh
This will generate the output in the ./outputs directory.
Or you can follow the innstructions of nnunetv2 to predict. they are equal.
More details are coming!