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+# 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
+
+---
+
+## 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)
+
+---
+
+## 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
+
+---
+
+## 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
+
+---
+
+## 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
+
+---
+
+## 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