complete artifacts 2026-07-17 10:13

.gitattributes

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 # Video files - compressed
 *.mp4 filter=lfs diff=lfs merge=lfs -text
 *.webm filter=lfs diff=lfs merge=lfs -text
+embeddings/emb_ids.json filter=lfs diff=lfs merge=lfs -text

README.md

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+---
+license: cc0-1.0
+tags: [medical-imaging, ct, intracranial-hemorrhage, rsna, conformal-prediction]
+---
+# ICH Phase-2 — trained models & artifacts
+
+RSNA-2019 winner-style cascade (2D CNN -> per-slice embeddings -> BiGRU) with three novelties
+(conformal risk control, cross-slice CAM-consistency, long-tail loss). Uploaded 2026-07-17.
+
+## Contents
+- `checkpoints/` — CNN + BiGRU heads (`cnn_best.pt`, `seq_winner_best.pt`, `seq_gate_best.pt`, `seq_sub_best.pt`)
+- `scores/` — val/test score arrays (`*.npz`) consumed by Step 3 (conformal + manuscript)
+- `embeddings/` — per-slice CNN embeddings (`emb.npy`, `emb_ids.json`) for regenerating sequence models
+- `run_config.json`, `class_stats.json` — full config + data statistics
+
+## Test-set results (macro-AUC)
+- BiGRU winner reproduction: 0.933 | our cascade: 0.932 | 2D-CNN baseline: 0.856
+- Epidural (rare class) AUC: 0.924

embeddings/emb.npy

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+size 1927173248

embeddings/emb_ids.json

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