hypertower/v3/docs/phase_plan.md

HyperTower Phase Plan

Phase 1 — PAPILA Baseline Reproduction

Goal: Reproduce the CNN results reported in the PAPILA paper and establish proper evaluation methodology.

Covers:

• Reproduce PAPILA paper AUC results for VGG16, MobileNetV2, ResNet50, DenseNet121, InceptionV3
• Present results with 95% CI and boxplot across repeated CV folds
• Show effect of data leakage: compare leaky CV (same patient in train and test) vs proper patient-stratified CV — motivates repeated CV methodology

Status: Complete

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Phase 2 — Image Preprocessing & Backbone Selection

Goal: Show the effect of image-level design choices on classification performance.

Covers:

• Show the effect of pre-training: Refugelike (REFUGE-pretrained) backbone vs standard ImageNet backbones
• Show the effect of cropping: U-Net optic disc crop vs no cropping, and crop scale sensitivity

Status: Complete — best config is refugelike backbone, no crop (proper CV)

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Phase 3 — Clinical Data Fusion (Single-Eye)

Goal: Show the effect of combining fundus image features with clinical metadata in the single-eye pipeline.

Covers:

• Show the effect of combining image data with clinical data: fused bridge vs image-only and clinical-only ablations
• Architecture search: loss function (BCD vs all-losses), SE attention, IOP correction strategy, feature ablations, network dimensions, dropout, learning rate, warmup strategy, augmentation, balanced sampling
• Epoch length sensitivity

Status: Complete — key findings: IOP ratio correction + drop raw (+1.7%), excluding axial length helps, age is most informative clinical feature, SE adds no benefit, LR very sensitive

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Phase 3.5 — Confirmation & Tuning

Goal: Confirm that the top phase 3 findings combine additively, and tune BCD probability with the best IOP preprocessing.

Covers:

• Combine iop_ratio_drop_raw (best preprocessing) with bcd_p07 (best loss setting)
• Extend BCD probability sweep to p=0.8 and p=0.9 to find the optimum

Planned approach:

• Baseline is phase 3 iop_ratio_drop_raw (0.8685 ± 0.011)
• All runs use best single-eye settings: refugelike, no crop, ratio IOP + drop raw, no axial length

Status: Not started

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Phase 4 — Dual CNN Architecture (Image Only)

Goal: Show the effect of processing both eyes jointly, and compare bilateral architectures against the single-eye baseline.

Covers:

• Show the effect of a dual CNN: bilateral tower (both eyes) vs single-eye tower, image data only
• Architecture comparison: canonical HyperTower bilateral (siamese shared-weight backbone returning mean+delta features) vs independent per-eye processing with late fusion

Planned approach:

• Image-only, no clinical data — isolates the bilateral vision question cleanly
• Use best image settings from phase 2 (refugelike, no crop)
• Compare siamese tower, independent bilateral, and single-eye (phase 3 baseline) directly

Status: Not started

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Phase 5 — Full HyperTower: Bilateral + Clinical Data + Fusion Heads

Goal: Bring together the best bilateral architecture (phase 4) with clinical data fusion (phase 3), and compare prediction aggregation strategies.

Covers:

• Show the effect of a dual CNN + clinical data: bilateral tower with fused clinical bridge — the full HyperTower model
• Ensemble vs fused head: patient-level prediction via ensemble (average OD+OS eye-level scores) vs learned fused head trained on top of single-eye scores

Planned approach:

• Use best settings from all prior phases (refugelike, no crop, ratio IOP + drop raw, no axial length)
• Compare tower modes: single, bilateral, ensemble, fused-head
• Establish final best configuration as the HyperTower result

Status: Not started