Repository for the paper "TriadNet: Sampling-free predictive intervals for lesional volume in 3D brain MR images" arxiv
This is a demonstration on a simple 3D image segmentation toy dataset.
- Step 1: Generate the toy dataset using generate_data.py
Images consist in 64x64x64 volumes with a sphere to segment, generated using TorchIO's RandomLabelsToImage function. To simulate the annotation uncertainty arising in medical tasks, the ground truth mask is randomly eroded or dilated.
- Step 2: Train a 3D TriadNet using train.py The training can be launched using:
python TriadNet/TriadNet/model/train.py TriadNet/TriadNet/model/config.yaml.
Don't forget to modify the paths of --output-folder and --data-csv in the YAML file.
- Step 3: Launch evaluation using test.py The script will first launch a calibration of the predictive intervals on calibration data, to find a corrective additive value to apply on the bounds to obtain a 90% coverage. Then, intervals are computed on the test images. Several metrics are computed: empiric coverage, average MAE, interval Width.
You can use a command such as: python TriadNet/TriadNet/model/test.py --run-folder path/to/trained/model
Below we present the performance for a run:
| Metric | Score |
|---|---|
| Target coverage | 0.900 |
| Empiric coverage | 0.914 |
| Average With | 43964 |
| Average MAE | 7786 |
- Citation: If you use this repository in your research please cite cite us ! arxiv