---
license: apache-2.0
pipeline_tag: image-segmentation
---

# nnActive: Toothfairy2 Queries and Analysis Results

This repository contains the queries and analysis results for the **Toothfairy2** dataset, as presented in the papers:

*   **Finally Outshining the Random Baseline: A Simple and Effective Solution for Active Learning in 3D Biomedical Imaging** (TMLR 2026). [[Paper]](https://huggingface.co/papers/2601.13677)
*   **nnActive: A Framework for Evaluation of Active Learning in 3D Biomedical Segmentation** (TMLR 2025). [[Paper]](https://arxiv.org/abs/2511.19183)

Official Code: [GitHub - MIC-DKFZ/nnActive](https://github.com/MIC-DKFZ/nnActive/tree/nnActive_v2)

## Summary
Active learning (AL) aims to reduce annotation costs in 3D biomedical image segmentation by selecting the most informative samples. However, standard uncertainty-based AL methods often struggle to outperform random baselines due to class imbalance and query redundancy. 

This work introduces **ClaSP PE** (Class-stratified Scheduled Power Predictive Entropy), a simple and effective query strategy that addresses these limitations through:
1.  **Class-stratified querying**: Ensures coverage of underrepresented structures.
2.  **Scheduled Power Noising**: Enforces query diversity in early-stage AL and encourages exploitation later.

The results in this repository were generated using the **nnActive** framework, an open-source evaluation framework for 3D medical AL built on top of nnU-Net.

## Artifact Information
The repository includes:
- `loop_XXX.json`: Lists of specific 3D patches selected for annotation in each Active Learning cycle.
- `dataset.json`: Metadata and label configuration for the Toothfairy2 experiment.
- `auc.json` & `summary.json`: Detailed performance metrics and Dice score statistics.
- `config.json`: Experimental parameters for reproducibility.

## Citation

If you use the **nnActive** framework or these results, please cite:

```bibtex
@article{luth2025nnactive,
    title={nnActive: A Framework for Evaluation of Active Learning in 3D Biomedical Segmentation},
    author={Carsten T. L{\"u}th and Jeremias Traub and Kim-Celine Kahl and Till J. Bungert and Lukas Klein and Lars Kr{\"a}mer and Paul F Jaeger and Fabian Isensee and Klaus Maier-Hein},
    journal={Transactions on Machine Learning Research},
    issn={2835-8856},
    year={2025},
    url={https://openreview.net/forum?id=AJAnmRLJjJ},
}
```

If you use **ClaSP PE**, please cite:

```bibtex
@article{luth2026finally,
    title={Finally Outshining the Random Baseline: A Simple and Effective Solution for Active Learning in 3D Biomedical Imaging},
    author={Carsten T. L{\"u}th and Jeremias Traub and Kim-Celine Kahl and Till J. Bungert and Lukas Klein and Lars Kr{\"a}mer and Paul F Jaeger and Klaus Maier-Hein and Fabian Isensee},
    journal={Transactions on Machine Learning Research},
    issn={2835-8856},
    year={2026},
    url={https://openreview.net/forum?id=UamXueEaYW},
}
```