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]
- nnActive: A Framework for Evaluation of Active Learning in 3D Biomedical Segmentation (TMLR 2025). [Paper]
Official Code: GitHub - MIC-DKFZ/nnActive
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:
- Class-stratified querying: Ensures coverage of underrepresented structures.
- 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:
@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:
@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},
}