rpotter6298
82 lines
3.7 KiB
Markdown
82 lines
3.7 KiB
Markdown
# HyperTower Phase Plan
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## Phase 1 — PAPILA Baseline Reproduction
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**Goal:** Reproduce the CNN results reported in the PAPILA paper and establish proper evaluation methodology.
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**Covers:**
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- Reproduce PAPILA paper AUC results for VGG16, MobileNetV2, ResNet50, DenseNet121, InceptionV3
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- Present results with 95% CI and boxplot across repeated CV folds
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- **Show effect of data leakage:** compare leaky CV (same patient in train and test) vs proper patient-stratified CV — motivates repeated CV methodology
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**Status:** Complete
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---
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## Phase 2 — Image Preprocessing & Backbone Selection
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**Goal:** Show the effect of image-level design choices on classification performance.
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**Covers:**
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- **Show the effect of pre-training:** Refugelike (REFUGE-pretrained) backbone vs standard ImageNet backbones
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- **Show the effect of cropping:** U-Net optic disc crop vs no cropping, and crop scale sensitivity
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**Status:** Complete — best config is refugelike backbone, no crop (proper CV)
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---
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## Phase 3 — Clinical Data Fusion (Single-Eye)
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**Goal:** Show the effect of combining fundus image features with clinical metadata in the single-eye pipeline.
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**Covers:**
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- **Show the effect of combining image data with clinical data:** fused bridge vs image-only and clinical-only ablations
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- 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
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- Epoch length sensitivity
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**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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---
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## Phase 3.5 — Confirmation & Tuning
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**Goal:** Confirm that the top phase 3 findings combine additively, and tune BCD probability with the best IOP preprocessing.
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**Covers:**
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- Combine `iop_ratio_drop_raw` (best preprocessing) with `bcd_p07` (best loss setting)
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- Extend BCD probability sweep to p=0.8 and p=0.9 to find the optimum
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**Planned approach:**
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- Baseline is phase 3 `iop_ratio_drop_raw` (0.8685 ± 0.011)
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- All runs use best single-eye settings: refugelike, no crop, ratio IOP + drop raw, no axial length
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**Status:** Not started
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---
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## Phase 4 — Dual CNN Architecture (Image Only)
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**Goal:** Show the effect of processing both eyes jointly, and compare bilateral architectures against the single-eye baseline.
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**Covers:**
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- **Show the effect of a dual CNN:** bilateral tower (both eyes) vs single-eye tower, image data only
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- **Architecture comparison:** canonical HyperTower bilateral (siamese shared-weight backbone returning mean+delta features) vs independent per-eye processing with late fusion
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**Planned approach:**
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- Image-only, no clinical data — isolates the bilateral vision question cleanly
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- Use best image settings from phase 2 (refugelike, no crop)
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- Compare siamese tower, independent bilateral, and single-eye (phase 3 baseline) directly
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**Status:** Not started
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---
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## Phase 5 — Full HyperTower: Bilateral + Clinical Data + Fusion Heads
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**Goal:** Bring together the best bilateral architecture (phase 4) with clinical data fusion (phase 3), and compare prediction aggregation strategies.
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**Covers:**
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- **Show the effect of a dual CNN + clinical data:** bilateral tower with fused clinical bridge — the full HyperTower model
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- **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
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**Planned approach:**
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- Use best settings from all prior phases (refugelike, no crop, ratio IOP + drop raw, no axial length)
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- Compare tower modes: single, bilateral, ensemble, fused-head
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- Establish final best configuration as the HyperTower result
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**Status:** Not started
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