Improve model card with metadata and links

Hi! I'm Niels, part of the community science team at Hugging Face.

This PR aims to improve the documentation of this repository by:
- Adding the `pipeline_tag: image-segmentation` to the metadata to help users find these artifacts.
- Linking the model card to the corresponding research paper on the Hugging Face Hub.
- Adding a link to the official GitHub repository.

Please review and merge if this looks good!

README.md

@@ -1,5 +1,57 @@
 ---
 license: apache-2.0
+pipeline_tag: image-segmentation
 ---
 
-Queries and analysis results for the Toothfairy2 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).
+# 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},
+}
+```