rpotter6298
708fbc70ce
- 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.
11 lines
1.1 KiB
JSON
11 lines
1.1 KiB
JSON
[
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{
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"_note": "Anonymous CV variant of the single-eye img+cd L1 fusion configuration at the refugelike R50 backbone (the 'classic hypertower' eye-level fusion mode without L2 bilateral aggregation). split_identity_level=0 disables patient-level fold grouping; fold assignment becomes patient-anonymous, allowing the two eyes of one patient to fall on opposite sides of the train/test split. Mirrors the image-only anonymous_cv_refugelike run (F2 block 2) but with the clinical tower fused in at the L1 stage, so the patient-anonymous CV inflation can be quantified directly against the single-eye img+cd headline number (ensemble_single_refugelike rep-mean test AUC 0.874 at split_identity_level=1). Same backbone, towers, bridge, training schedule, and seeds as the single-eye headline; only the fold-grouping rule changes. Bilateral L2 is not included because patient-level aggregation is mechanically incompatible with patient-anonymous fold assignment.",
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"run_name": "experiments/backbone_replication/anonymous_cv_ensemble_single_refugelike",
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"reps": 10,
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"overrides": {
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"split_identity_level": 0
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}
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}
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]
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