The model uses a frozen ImageNet-pretrained ResNet-18 backbone with a task-specific classification head. The key innovation is the explicit integration of clinician-defined anatomical Regions of Interest (ROI) — specifically the lower eyelid margin and eyelash–cornea interface — as a prior constraint, enabling robust classification in a small-data regime (~80–150 cases per class).

Architecture

Input (224×224 RGB)
    │
    ▼
ResNet-18 backbone (frozen, ImageNet pretrained)
    │  layer1 → layer2 → layer3 → layer4
    │  Global Average Pooling → (512,)
    ▼
Dropout(0.3) → Linear(512 → 2)
    │
    ▼
Softmax → [P(control), P(epiblepharon)]

Component	Detail
Backbone	ResNet-18 (ImageNet pretrained, fully frozen)
Classification head	`Dropout(0.3)` + `Linear(512 → 2)`
Loss function	`CrossEntropyLoss`
Optimizer	`Adam(lr=1e-3)`, head parameters only
Input size	224 × 224 px, RGB (Grayscale → 3ch conversion applied)
Normalization	ImageNet mean/std `[0.485, 0.456, 0.406]` / `[0.229, 0.224, 0.225]`

Performance

Evaluated by stratified 5-fold cross-validation (random_state=42, 20 epochs/fold).

Metric	Mean (5-fold)
AUC	0.93
Accuracy	High
Sensitivity	High
Specificity	Moderate
F1 Score	High

✅ No fold collapse observed across all 5 folds
✅ Label-shuffling negative control confirmed genuine feature learning
✅ ROI ablation experiments validated lower eyelid margin as primary diagnostic signal

ROI Ablation Summary

Condition	Performance vs Baseline
Full image (baseline)	✅ Optimal
ROI ablated (lower eyelid blurred)	❌ Significant drop
Non-ROI ablated (ROI preserved)	✅ Near-baseline

Diagnostic features are spatially localized to the clinically defined lower eyelid margin — consistent with clinical examination principles for epiblepharon.

Grad-CAM Explainability

Grad-CAM heatmaps were generated using native PyTorch hooks on layer4 (no Captum dependency):

Epiblepharon cases: Activation consistently focused on lower eyelid margin and eyelash–cornea interface
Control cases: Diffuse, anatomically unfocused activation patterns

Heatmap overlay: α = 0.45, JET colormap, bilinear upsampling to 224×224.

iOS On-Device Inference

The trained model has been converted to Apple Core ML format (.mlpackage):

Metric	Value
Model size	< 50 MB
Inference latency	< 1 second / image
Device	iPhone 12+ (A14+ Neural Engine)
Network required	❌ None — fully on-device

Privacy: facial images never leave the device, consistent with PDPA / HIPAA principles.

Training Data

Task: Binary classification — epiblepharon vs. control
Image type: External eye photographs
Dataset size: ~80–150 cases per class (single-center, retrospective)
Preprocessing: Resize 224×224, Grayscale→3ch, ColorJitter, RandomHorizontalFlip, ImageNet normalization

⚠️ Clinical images are not distributed in this repository due to patient privacy regulations (Personal Data Protection Act, IRB). For academic collaboration, please contact the corresponding author.

Usage

python

import torch
from torchvision import models, transforms
from PIL import Image

# Load model
model = models.resnet18(weights=None)
for param in model.parameters():
    param.requires_grad = False
model.fc = torch.nn.Linear(model.fc.in_features, 2)
model.load_state_dict(torch.load("pytorch_model.pt", map_location="cpu"))
model.eval()

# Preprocess
transform = transforms.Compose([
    transforms.Resize((224, 224)),
    transforms.Grayscale(num_output_channels=3),
    transforms.ToTensor(),
    transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225]),
])

img = Image.open("eye_photo.jpg").convert("RGB")
x = transform(img).unsqueeze(0)  # (1, 3, 224, 224)

with torch.no_grad():
    logits = model(x)
    prob = torch.softmax(logits, dim=1)[0, 1].item()
    print(f"Epiblepharon probability: {prob:.3f}")

Files in This Repository

File	Description
`README.md`	This model card
`model.py`	Model architecture definition
`train.py`	5-fold cross-validation training script
`config.json`	Model configuration
`requirements.txt`	Python dependencies
`pytorch_model.pt`	(Checkpoint — upload separately after training)

Intended Use & Limitations

Intended use: Research prototype for clinical decision support in epiblepharon screening
NOT a validated medical device — prospective evaluation and regulatory assessment required before clinical deployment
Single-center retrospective data — generalizability across imaging conditions and demographics requires multi-center validation

Citation

bibtex

@misc{elias2026,
  title     = {ELIAS: Eyelid Lesion Intelligent Analysis System},
  year      = {2026},
  note      = {2026 MOEA Smart Innovation Award submission},
  url       = {https://huggingface.co/YOUR_HF_USERNAME/ELIAS-epiblepharon}
}

License

MIT License — Source code only. Clinical data excluded.

cahsu/ELIAS-epiblepharon

ELIAS — Eyelid Lesion Intelligent Analysis System

Model Description