Add analysis scripts and experiment configurations for bridge attention and sensitivity studies

- Introduced `bridge_attention_ceiling_check.py` for variance decomposition analysis on bridge attention configurations. - Added `bridge_attention_readout.py` to perform per-tower gate and contribution readouts, including AUC sanity checks. - Created multiple JSON configuration files for backbone replication experiments, including anonymous CV variants and basic backbones. - Implemented sensitivity experiments to evaluate the impact of axial length inclusion and EfficientNetV2-M performance at higher resolutions. - Added a memory probe script to assess GPU memory usage during training with EfficientNetV2-M.
2026-07-03 08:51:44 +02:00
parent 3d954a4606
commit 708fbc70ce
52 changed files with 2223 additions and 218 deletions
@@ -58,9 +58,9 @@ SEV_COLORS = {
    "unknown":  C_UNKNOWN,
 }

-SEV_ORDER = ["normal", "unknown", "early", "moderate", "severe"]
-SEV_ALPHA = {"normal": 0.40, "unknown": 0.35, "early": 0.55, "moderate": 0.70, "severe": 0.85}
-SEV_SIZE  = {"normal": 6,    "unknown": 6,    "early": 8,    "moderate": 10, "severe": 12}
+SEV_ORDER = ["severe", "moderate", "unknown", "early", "normal"]
+SEV_ALPHA = {"normal": 0.55, "unknown": 0.55, "early": 0.55, "moderate": 0.55, "severe": 0.55}
+SEV_SIZE  = {"normal": 8,    "unknown": 8,    "early": 8,    "moderate": 8,    "severe": 8}

 # ── Per-panel definitions: (label, results dir, eval_stage) ──────────────────
 # Top row: single-modality reference runs