📄 Progressive Pseudo-labels Enhancement for Source-Free Domain Adaptation Medical Image Segmentation

Dependency Preparation

cd ESFDA_SFDA
# Python Preparation
conda create -n ESFDA_SFDA python=3.8.5
activate ESFDA_SFDA
# (torch 1.7.1+cu110) It is recommended to use the conda installation on the Pytorch website https://pytorch.org/
conda install pytorch==1.7.1 torchvision==0.8.2 torchaudio==0.7.2 cudatoolkit=11.0 -c pytorch
pip install -r requirements.txt

Model Training and Inference

• 1. Download the dataset and modify the relevant paths in the configuration file.
• 2. Source Model Train -- We use the code provided by ProSFDA to train the source model.
• 3. Generation phase: Generate target domain pseudo-labels

          python generate_pseudo.py

• 4. Adaptation stage: the source model adapts to the target domain

          python Train_target.py

• 5. Validation stage: You can get the pre-trained weights at https://drive.google.com/file/d/1XOjpNu_SO40vKy820OYRpe5ZVgUtNubD/view?usp=drive_link and validate it.

          python Validate.py