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
647 B
CSV
11 lines
647 B
CSV
feature,mean_drop,std_drop,baseline_auc_mean
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Age,0.1487745098039216,0.07246596166317897,0.7117647058823529
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IOP_corr,0.0645588235294118,0.053595964337907635,0.7117647058823529
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Phakic/Pseudophakic,0.031004901960784353,0.05039528009700277,0.7117647058823529
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Pachymetry,0.011421568627451003,0.016547911692771797,0.7117647058823529
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Gender,0.006102941176470622,0.022596178307964714,0.7117647058823529
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eyeID,0.0,0.0,0.7117647058823529
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dioptre_2,-0.0010294117647058861,0.003622645200240852,0.7117647058823529
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astigmatism,-0.002156862745098008,0.008336072214271729,0.7117647058823529
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dioptre_1,-0.006593137254901939,0.011003619078899692,0.7117647058823529
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