TEDM-demo / README.md
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metadata
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.