rpotter6298
3d7777f010
- Introduced `fused_importance_with_axial.py` to evaluate the importance of Axial_Length in the fused-head model. - Created JSON configurations for various experiments excluding zero-importance clinical features: - `cd_solo_bilateral_dropzero.json`: Bilateral clinical-only evaluation. - `cd_solo_single_dropzero.json`: Single-eye clinical-only evaluation. - `ensemble_refugelike_ckpt_dropzero.json`: Ensemble model with dropped zero-importance features. - `ensemble_single_refugelike_dropzero.json`: Single-eye ensemble model with dropped features.
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Future Directions
Deferred ideas that are out of scope for the current manuscript but worth revisiting once the paper is out. Nothing here changes headline numbers; the fused HyperTower already beats the Kovalyk-Borodyak reproduction comfortably. These are things we noticed while building binocular_analog/ that could tighten the image-only or bilateral tower further.
Image tower / bilateral fusion (from Kovalyk repro comparison)
Context: their end-to-end binocular ResNet-50 reproduction lands at ~0.86 bilateral AUC on PAPILA, which is above what our own img_solo bilateral image tower gets. The gap is not evidence that their architecture is better; it points at four training choices we haven't yet copied. Ranked by expected effect:
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Backbone: plain ImageNet V1 ResNet-50 instead of REFUGE-pretrained
refugelike. In the Kovalyk repro we saw V1 > V2 on PAPILA (opposite of ImageNet-accuracy prediction). Same logic could apply to REFUGE pretraining: the REFUGE checkpoint may over-specialize early features to a different fundus camera / cohort, and plain ImageNet may transfer more cleanly. Trybackbone: "resnet50"onimg_solowith everything else held fixed. -
Backpropagate the patient-level loss through the backbone. In
img_solo, thehbstage does not settrain_towers: true, so the patient-level loss can only reshape the 4096→256 HyperBridge projection and the head. Kovalyk's BinoCNN is jointly trained end-to-end. Enable tower training on thehbstage (probably with a smaller LR to avoid destabilising the eye-level warmup) and see if the bilateral AUC moves. -
Wider patient-level fusion. HyperBridge
embedding_mlpcompresses 4096→256. Kovalyk's head goes 4096→512→2 with a real hidden layer. Tryhidden_dim: 512and stack a small MLP head; cheap ablation. -
Label smoothing + explicit class weighting in the head loss. Kovalyk uses
CrossEntropyLoss(weight=inv_freq, label_smoothing=0.14). Ours is BCE-with-logits, no smoothing, no class weight. On a 3.5:1 imbalance at this dataset size, adding smoothing (~0.1) and a pos_weight tends to stabilise the minimizer. Cheap to try.
NOT on the list: disc-region ROI cropping (GT contour bbox or U-Net-derived bbox). Kovalyk pre-crops to a 299×299 square ROI, but our own crop-sweep experiments (v4/results/experiments/backbone_replication/gtcrop_refugelike, unetcrop_refugelike) showed cropping hurts on PAPILA. Do not add it back as an image-tower improvement.
Compute / infrastructure (already documented)
See memory/project_future_v4_compute_fixes.md for the two deferred v4 speedups:
- Lazy BCD tower forward (~33% nt savings)
- Frozen-prefix embedding cache for hb/val (foundation for a future feature-cache service)
Reproduction extensions (nice-to-have, not blocking)
- Fill in the remaining 6 CNN configs from Kovalyk-Borodyak (VGG16, InceptionV3, MobileNetV2, mono + bino each) if we ever want to report a fuller reproduction table rather than a single ResNet-50 headline.
- Try the paper's freeze-count under a proper Ray Tune HPO context; our 0.86-0.88 vs their 0.764 is the persistent unexplained residual, and the freeze-mode ablation did not close it.