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README.md

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 license: apache-2.0
+pipeline_tag: image-segmentation
 ---
 
-Queries and analysis results for the WORD dataset, as presented in the papers [https://arxiv.org/abs/2511.19183](https://arxiv.org/abs/2511.19183) and [https://arxiv.org/abs/2601.13677](https://arxiv.org/abs/2601.13677).
+# WORD Dataset Queries and Analysis Results
+
+This repository contains queries and analysis results for the **WORD** dataset, as presented in the papers:
+
+- **Finally Outshining the Random Baseline: A Simple and Effective Solution for Active Learning in 3D Biomedical Imaging** ([Arxiv](https://arxiv.org/abs/2601.13677))
+- **nnActive: A Framework for Evaluation of Active Learning in 3D Biomedical Segmentation** ([Arxiv](https://arxiv.org/abs/2511.19183))
+
+Official Code: [GitHub - MIC-DKFZ/nnActive](https://github.com/MIC-DKFZ/nnActive)
+
+## Summary
+Active learning (AL) has the potential to drastically reduce annotation costs in 3D biomedical image segmentation. This work introduces **Class-stratified Scheduled Power Predictive Entropy (ClaSP PE)**, a query strategy that addresses class imbalance and redundancy. Within the **nnActive** framework, the authors demonstrate that ClaSP PE consistently outperforms improved random baselines in terms of both performance and annotation efficiency in realistic scenarios.
+
+## Sample Usage
+
+To setup and run experiments using the `nnactive` framework, you can use the following CLI commands:
+
+```bash
+# Setup an experiment
+nnactive setup_experiment --experiment word__tr-nnActiveTrainer_500epochs__patch-29_74_87__sb-random-label2-all-classes__sbs-800__qs-800__unc-random__seed-12348
+
+# Run the experiment
+nnactive run_experiment --experiment word__tr-nnActiveTrainer_500epochs__patch-29_74_87__sb-random-label2-all-classes__sbs-800__qs-800__unc-random__seed-12348
+```
+
+To analyze existing experiments:
+
+```bash
+nnactive analyze_experiments --base_path $nnActive_results --raw_path $nnActive_data --output_path {OUTPUT_PATH}
+```
+
+## Citation
+If you use these results or the nnActive framework, please cite the following work:
+
+```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},
+    year={2026},
+    url={https://openreview.net/forum?id=UamXueEaYW},
+}
+
+@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},
+    year={2025},
+    url={https://openreview.net/forum?id=AJAnmRLJjJ},
+}
+```