rpotter6298
24 lines
1.5 KiB
Markdown
24 lines
1.5 KiB
Markdown
# Project TODO
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## Paper
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- [ ] **Learning curve analysis** — train on 25/50/75/100% of training data, plot AUC vs n.
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Motivation: empirical evidence that the model is data-starved, which justifies the decision
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not to pursue attention-gating (transformer) extensions to the NTowerHT bridge.
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If the curve is still ascending at full data → supports the argument that a more expressive
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architecture would overfit at this sample size. Generates a figure for the paper.
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- [ ] **GradCAM nasal-side analysis** — re-run GradCAM separately for OD and OS eyes rather
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than aggregated. The current aggregation mirrors the two eyes against each other, washing out
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any directional bias. Clinically, we would expect GradCAM attention offset from the disc center
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to trend toward the nasal side (where RNFL loss presents earliest in glaucoma). If the model
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has learned this, it would only be visible in per-side heatmaps — OD and OS are mirror images
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so the nasal direction is opposite for each. This could be a strong interpretability result
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for the paper if the bias is present.
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- [ ] **Quantify attention-gating as future work** — use the learning curve result + parameter
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count ratio (Q/K/V projections over fusion_dim vs training n) to formally justify the choice.
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Frame in paper as: "we identify cross-attention inside the NTowerHT bridge as a promising
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extension, but our sample size (N≈400 training patients) is insufficient to avoid overfitting
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a more expressive interaction layer" — cite the learning curve figure as evidence.
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