README.md

metadata

title: AMDRisk
emoji: 📉
colorFrom: indigo
colorTo: pink
sdk: gradio
sdk_version: 6.14.0
python_version: '3.13'
app_file: app.py
pinned: false
license: mit
short_description: Predicting risk of late AMD using Deep Learning

AMD Risk Prediction

PyTorch reimplementation of the DeepSeeNet-based AMD progression risk framework from Peng et al., npj Digital Medicine 2020, “Predicting risk of late age-related macular degeneration using deep learning.”

• Extracts DeepSeeNet hidden features from drusen and pigment abnormality models.
• Fits Cox proportional hazards models using image-derived features plus age and smoking status.

Feature Extraction

• Input: paired baseline CFPs

  • LE_PATHNAME
  • RE_PATHNAME

• Models used:

  • deepseenet/weights/drus.pt
  • deepseenet/weights/pig.pt

• Image preprocessing:

  • RGB conversion
  • validation transform from deepseenet/augmentations.py
  • default input size: 1024 × 1024

• Feature source:

  • penultimate layer of each DeepSeeNet classifier
  • final linear layer input captured by forward hook

• Feature layout:

LE_DRUS_000 ... LE_DRUS_127
RE_DRUS_000 ... RE_DRUS_127
LE_PIG_000  ... LE_PIG_127
RE_PIG_000  ... RE_PIG_127

• Total feature dimension:

128 × 2 models × 2 eyes = 512 features / patient

• Output:

data/areds1_deepseenet_features.npz

• Stored arrays:
  • features: (N, 512)
  • patids: (N,)
  • feature_names: (512,)

Cox Model Training

• Survival labels from endpoint-specific JSON files:

  • Status_late_amd
  • Status_anyga
  • Status_nv

• Time-to-event column:

  • Survival_in_years

• Predefined fold split:

  • train: folds 3, 4, 5, 6, 7, 8, 9
  • validation: fold 2
  • test: folds 0, 1

• Stage 1 baseline:

  • structured grading features only

LE_DRUS
RE_DRUS
LE_PIG
RE_PIG
age
smkever

• Stage 2 DeepSeeNet features:

  • selected hidden features from 512-dimensional feature vector
  • plus age
  • plus smkever

• Preprocessing:

  • low-variance feature filtering
  • train-only StandardScaler
  • same scaler applied to validation/test

• Cox model:

  • lifelines.CoxPHFitter
  • L2 penalization
  • default penalizer: 0.01

• Feature-selection tweaks:

  • global top-k selection

    • rank features by univariate train-set concordance
    • examples: --top-k 8, --top-k 16

  • block-balanced top-k selection

    • select top-k features separately from each block:

LE_DRUS_*
RE_DRUS_*
LE_PIG_*
RE_PIG_*

• Block-balanced example:

--top-k-per-block 4

4 LE_DRUS features
4 RE_DRUS features
4 LE_PIG features
4 RE_PIG features
+ age
+ smkever
= 18 total features

• Rationale:

  • avoids one highly correlated feature block dominating global top-k
  • improves stability of Cox fitting
  • closer in spirit to grouped/correlation-aware feature selection