- 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.
- Introduced multiple regression experiment configurations targeting vf_md, including:
- cd_solo_reg_set.json: CD tower only regression setup.
- img_solo_reg_set.json: Image tower only regression setup.
- reg_head_epoch_sweep.json: Baseline regression sweeps at different epochs (50, 75, 100).
- reg_head_set.json: Various regression setups including baseline and OrthoBridge configurations.
- single_eye_reg.json: Single-eye regression setup for worst-eye aggregation analysis.
- Added ensemble configurations for OrthoBridge with different inner bridges:
- ortho_alts_ensemble.json: Ensemble tests with ConcatBridge, PairwiseAdditiveBridge, and GatedAdditiveBridge.
- ortho_alts_tritower.json: Tritower tests with the same inner bridges.
- Created V2-M specific configurations:
- baseline_reg_nt50.json: Regression baseline with V2-M backbone.
- geom_vec_gt.json and geom_vec_unet.json: Geometry vector injection experiments with V2-M.
- single_l1_bridges.json: Single-eye ensemble experiments with various bridge types.
- tritower_geom_gt.json: Tritower setup with GT contour-rasterized masks.
- Promoted existing experiments to higher repetitions for robustness.
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