Improve model card metadata and content

Hi, I'm Niels from the Hugging Face community team. This PR improves the model card by adding relevant metadata and linking it to the associated research paper. It also adds a link to the official code repository and provides sample usage instructions based on the GitHub README to improve the artifact's discoverability and usability.

README.md

@@ -1,5 +1,55 @@
 ---
 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},
+}
+```