Add dataset card with license (MIT for team code, CT-NEXUS bundled as-is)

README.md

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+license: mit
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+# CVPR 2026 CT-FM Challenge — Task 2 EAO, Vision-Only Track Submission
+
+Submission artifact for **Aviral** (team leader: Aviral Kaintura) to the *CVPR 2026: Foundation Models for General CT Image Diagnosis* challenge (Codabench competition [12650](https://www.codabench.org/competitions/12650/)), Task 2 — Embedding Aggregation Optimization (EAO), Vision-Only (VO) track.
+
+Validation result: **0.7086 balanced accuracy / 0.728 AUROC**, mean over 17 binary classification targets (Codabench submission #715408).
+
+## Contents
+
+- `aviral_vo_eao.tar.gz` — Docker image (`aviral_vo_eao:latest`, ~13GB). Wraps the organizer-provided **CT-NEXUS** backbone as a frozen feature extractor; entrypoint `extract_feat_EAO.sh` produces per-case spatial embeddings.
+- `aviral_vo_eao_inference.zip` — training script (`run_EAO_improved.py`), ensemble inference script (`predict_eao_ensemble.py`), trained checkpoints (4 seeds × 17 targets × multiple learning rates), and a standalone threshold-optimization script.
+- `predict_eao_ensemble.py` — standalone copy of the final inference script, with per-target decision thresholds merged in directly so it runs as a single step (no separate threshold-optimization pass required).
+
+## License
+
+The team's own contributions in this repository — training code, inference/ensembling code, threshold-calibration logic, and trained checkpoint weights — are released under the **MIT License**.
+
+**This does not cover the CT-NEXUS backbone bundled inside the Docker image.** CT-NEXUS is an organizer-provided baseline for this challenge ([github.com/kmin940/CT-NEXUS](https://github.com/kmin940/CT-NEXUS)) with no separate publication, model card, or license file of its own. It is included here only as required to reproduce this submission's Docker container; if you intend to reuse the backbone itself outside the context of this challenge, refer to its source repository directly rather than assuming MIT terms apply to it.
+
+## Citation
+
+If you reference this submission, please cite the challenge itself:
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+> CVPR 2026 Workshop — Foundation Models for General CT Image Diagnosis, Codabench competition 12650.