bhargavvz/SkinGuard-AI

🏥 SkinGuard AI

Production-Grade Skin Cancer Detection with Deep Learning

EVA-02 + ConvNeXt-V2 + Swin-V2 Ensemble | ISIC 2019 | H100 Optimized

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🔬 Overview

SkinGuard AI is a production-ready deep learning system for dermoscopic skin lesion classification. It identifies 8 types of skin lesions including melanoma, basal cell carcinoma, and squamous cell carcinoma with >95% accuracy.

Key Features

Feature	Description
🧠 3-Model Ensemble	EVA-02-Large (304M) + ConvNeXt-V2-Large (198M) + Swin-V2-Base (87M)
📊 ISIC 2019 Dataset	25,331 dermoscopic images, 8 classes — loaded from Hugging Face
⚡ H100 Optimized	BF16, torch.compile, TF32, gradient accumulation — 7hr budget
🔥 Grad-CAM	Explainable AI — see where the model looks
🎯 Test-Time Augmentation	8× augmentation averaging for robust predictions
📦 ONNX Export	Production deployment with benchmarking
🌐 Gradio Web Demo	Interactive UI with risk assessment and confidence bars
🐳 Docker Ready	Multi-stage CUDA container for deployment

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📋 Skin Lesion Classes

Class	Full Name	Risk Level
MEL	Melanoma	🔴 HIGH
BCC	Basal Cell Carcinoma	🔴 HIGH
SCC	Squamous Cell Carcinoma	🔴 HIGH
AK	Actinic Keratosis	🟠 MEDIUM
BKL	Benign Keratosis	🟢 LOW
NV	Melanocytic Nevus	🟢 LOW
DF	Dermatofibroma	🟢 LOW
VASC	Vascular Lesion	🟢 LOW

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🏗️ Architecture

┌──────────────────────────────────────────────────────┐
│                   Input Image (384×384)               │
├──────────┬──────────────┬────────────────────────────┤
│ EVA-02-L │ ConvNeXt-V2-L│    Swin-V2-Base            │
│ (304M)   │   (198M)     │     (87M)                  │
│ ViT      │   CNN        │    Window Attn              │
├──────────┴──────────────┴────────────────────────────┤
│           Attention-Weighted Fusion                    │
│    (Learnable weights per backbone per sample)        │
├──────────────────────────────────────────────────────┤
│         Classification Head                           │
│    LayerNorm → Linear → GELU → Dropout → Linear      │
├──────────────────────────────────────────────────────┤
│           8-Class Softmax Output                      │
└──────────────────────────────────────────────────────┘

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🚀 Quick Start

1. Install Dependencies

pip install -r requirements.txt

2. Train the Model

# One-click training (H100 optimized, 7hr budget)
bash scripts/train.sh

# Or with custom flags
python -m src.trainer --config configs/config.yaml

# Quick smoke test
python -m src.trainer --config configs/config.yaml --smoke-test

3. Evaluate

# Full evaluation with metrics, plots, and ONNX export
bash scripts/evaluate.sh

# Or manually
python -m src.evaluate --config configs/config.yaml --checkpoint outputs/checkpoints/best.pth

4. Inference

# CLI inference with risk assessment
python -m src.inference --image path/to/lesion.jpg --config configs/config.yaml

# JSON output (for API integration)
python -m src.inference --image path/to/lesion.jpg --json

# With Grad-CAM
python -m src.inference --image path/to/lesion.jpg --gradcam

5. Web Demo

python app/gradio_app.py
# Open http://localhost:7860

6. Export to ONNX

python -m src.export --config configs/config.yaml --benchmark

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🏋️ Training Details

H100 Optimizations

Optimization	Setting
Precision	BF16 (bfloat16)
Compilation	torch.compile(mode='reduce-overhead')
TF32	Enabled for matmul
Grad Accumulation	4 steps (effective batch = 256)
Memory Format	Channels Last
cuDNN	Benchmark mode

Training Strategy

• Phase 1 (Epochs 1-5): Backbones frozen — train fusion + classifier head only
• Phase 2 (Epochs 6+): Backbones unfrozen — fine-tune with 10× lower backbone LR
• Early Stopping: Patience=8 on validation AUROC
• Time Watchdog: Auto-saves and stops before 7h limit

Augmentation Pipeline

Training: RandomResizedCrop, HorizontalFlip, VerticalFlip, ShiftScaleRotate, HueSaturationValue, RandomBrightnessContrast, CLAHE, GaussNoise, CoarseDropout, ElasticTransform

TTA (Test-Time): Original + HFlip + VFlip + HVFlip + Rot90 + Rot180 + Rot270 + ScaleUp → Average

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📁 Project Structure

nre/
├── configs/
│   └── config.yaml              # All hyperparameters
├── src/
│   ├── dataset.py               # HF dataset + augmentations
│   ├── models.py                # 3-backbone ensemble
│   ├── losses.py                # Focal loss + mixup/cutmix
│   ├── trainer.py               # H100-optimized training
│   ├── evaluate.py              # Metrics & visualizations
│   ├── gradcam.py               # Grad-CAM heatmaps
│   ├── inference.py             # Production inference
│   ├── tta.py                   # Test-Time Augmentation
│   └── export.py                # ONNX export + benchmark
├── app/
│   └── gradio_app.py            # Web demo
├── scripts/
│   ├── train.sh                 # Training launcher
│   └── evaluate.sh              # Evaluation launcher
├── tests/
│   └── test_pipeline.py         # Smoke tests
├── requirements.txt
├── Dockerfile
└── README.md

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🐳 Docker

# Build
docker build -t skinguard-ai .

# Run (with GPU)
docker run --gpus all -p 7860:7860 \
  -v $(pwd)/outputs:/app/outputs \
  skinguard-ai

# Training in Docker
docker run --gpus all \
  -v $(pwd)/outputs:/app/outputs \
  skinguard-ai bash scripts/train.sh

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📊 Expected Results

Metric	Target	Notes
Overall Accuracy	>95%	With TTA
Weighted AUROC	>0.98	One-vs-rest
Melanoma Recall	>92%	Critical for safety
Training Time	<7 hours	On H100 GPU
Inference	<50ms	Per image (GPU)

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⚠️ Disclaimer

This system is for educational and research purposes only. It is NOT a medical device and should NOT be used as a substitute for professional medical diagnosis. Always consult a qualified dermatologist for skin lesion evaluation.

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🙏 Acknowledgements

• ISIC 2019 dataset (International Skin Imaging Collaboration)
• timm library by Ross Wightman
• Hugging Face datasets library
• Albumentations for image augmentations
• Gradio for the web interface

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Made with ❤️ using PyTorch, Hugging Face, and timm