Segmentation Consistency Training:Out-of-Distribution Generalization for Medical Image Segmentation

This repository is the official implementation of Segmentation Consistency Training:Out-of-Distribution Generalization for Medical Image Segmentation. The paper is available here

Requirements

To install requirements:

pip install -r requirements.txt

Training

To train an instance of a predictor with a given model architecture and an ID number (to name the saved models), run:

python train.py [Model Architecture] [ID]

To train ten predictors for each model architecture, run:

bash run_paper_experiments.sh

Evaluation

To evaluate the models as trained using the above script, run:

python eval.py [ID lower] [ID upper]

This will evaluate the models given with IDs in the range [ID lower, ID ipper]

Results

We demonstrate that Segmentation Inconsistency Training improves generaliation by a statistically significant margin (p>0.99) on all three tested out-of-distribution datasets.

##Contact information For questions or help, feel free to contact me at birk.s.torpmann-hagen@uit.no.