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---
title: TEDM
emoji: 🐨
colorFrom: purple
colorTo: yellow
sdk: gradio
sdk_version: 3.35.2
app_file: app.py
pinned: false
license: mit
---
# Robust semi-supervised segmentation with timestep ensembling diffusion models
Results
| Training data size | 1 (1\%) | 3 (2\%) | 6 (3\%) | 12 (96\%) | 197 (100\%) |
|:------------------|:----------------------------------------------:|:-----------------------:|:-----------------------:|:-----------------------:|:-----------------------:|
| |JSRT (labelled in-domain) |
| Baseline | 84.4 $\pm$ 5.4 | 91.7 $\pm$ 3.7 | 93.3 $\pm$ 2.9 | 95.3 $\pm$ 2.3 | 97.3 $\pm$ 1.2 |
| LEDM | 90.8 $\pm$ 3.5 | 94.1 $\pm$ 1.6 | 95.5 $\pm$ 1.4 | 96.4 $\pm$ 1.4 | 97.0 $\pm$ 1.3 |
| LEDMe | **93.7 $\pm$ 2.6** | **95.5 $\pm$ 1.5** | **96.7 $\pm$ 1.5** | **97.0 $\pm$ 1.1** | **97.6 $\pm$ 1.2** |
| Ours | **93.1 $\pm$ 3.4** | 94.8 $\pm$ 1.4 | 95.8 $\pm$ 1.2 | 96.6 $\pm$ 1.1 | 97.3 $\pm$ 1.2 |
| |NIH (unlabelled in-domain) |
| Baseline | 68.5 $\pm$ 12.8 | 71.2 $\pm$ 15.1 | 71.4 $\pm$ 15.9 | 77.8 $\pm$ 14.0 | 81.5 $\pm$ 12.7 |
| LEDM | 63.3 $\pm$ 12.2 | 78.0 $\pm$ 10.1 | 81.2 $\pm$ 9.3 | 85.9 $\pm$ 7.4 | 88.9 $\pm$ 5.9 |
| LEDMe | 70.3 $\pm$ 11.4 | 78.3 $\pm$ 9.8 | 83.0 $\pm$ 8.6 | 84.4 $\pm$ 8.1 | 90.1 $\pm$ 5.3 |
| Ours | **80.3 $\pm$ 9.0** | **86.4 $\pm$ 6.2** | **89.2 $\pm$ 5.5** | **91.3 $\pm$ 4.1** | **92.9 $\pm$ 3.2** |
| | Montgomery (out-of-domain) |
| Baseline | 77.1 $\pm$ 12.0 | 83.0 $\pm$ 12.2 | 80.9 $\pm$ 14.7 | 83.8 $\pm$ 14.9 | 94.1 $\pm$ 6.6 |
| LEDM | 79.3 $\pm$ 8.1 | 85.9 $\pm$ 7.4 | 89.4 $\pm$ 6.7 | 92.3 $\pm$ 7.2 | 94.4 $\pm$ 7.2 |
| LEDMe | 80.7 $\pm$ 6.6 | 86.3 $\pm$ 6.5 | 89.5 $\pm$ 5.9 | 91.2 $\pm$ 5.6 | **95.3 $\pm$ 4.0** |
| Ours | **90.5 $\pm$ 5.3** | **91.4 $\pm$ 6.1** | **93.3 $\pm$ 6.0** | **94.6 $\pm$ 6.0** | 95.1 $\pm$ 6.9 |
## Training
- training the backbone
```python train.py --dataset CXR14 --data_dir <PATH TO CXR14 DATASET>```
- our method
```python train.py --experiment TEDM --data_dir <PATH TO JSRT DATASET> --n_labelled_images <TRAINING SET SIZE>```
- LEDM method
```python train.py --experiment LEDM --data_dir <PATH TO JSRT DATASET> --n_labelled_images <TRAINING SET SIZE>```
- LEDMe method
```python train.py --experiment LEDMe --data_dir <PATH TO JSRT DATASET> --n_labelled_images <TRAINING SET SIZE>```
- baseline method
```python train.py --experiment JSRT_baseline --data_dir <PATH TO JSRT DATASET> --n_labelled_images <TRAINING SET SIZE>```
## Testing
- update
- `DATADIR` in paths `dataloaders/JSRT.py`, `dataloaders/NIH.py` and `dataloaders/Montgomery.py`
- `NIHPATH`, `NIHFILE`, `MONPATH` and `MONFILE` in paths `auxiliary/postprocessing/run_tests.py` and `auxiliary/postprocessing/testing_shared_weights.py`
- for baseline and LEDM methods, run
```python auxiliary/postprocessing/run_tests.py --experiment <PATH TO LOG FOLDER>```
- for our method, run
```python auxiliary/postprocessing/testing_shared_weights.py --experiment <PATH TO LOG FOLDER>```
## Figures and reporting
VS Code notebooks can be found in `auxiliary/notebooks_and_reporting`.
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