skin-lesion-ensemble

7-Model Ensemble for Skin Lesion Classification

8クラス皮膚病変分類のための7モデルアンサンブルシステム Test Accuracy: 95.92%

Model Description

35個のディープラーニングモデル（7種類 × 5-fold CV）を組み合わせた高精度な皮膚病変診断支援システムです。

Target Classes (8 classes)

Available Model Configurations

このリポジトリには2つのモデル構成が含まれています：

Base Models (20 models) - バイナリなし版

base_models/
├── efficientnet_b4_ham10k/      # EfficientNet-B4 × 5
├── efficientnet_b7_imagenet/    # EfficientNet-B7 × 5
└── transformers/                 # ViT × 5, Swin × 5

Full Ensemble (35 models) - フル版

├── efficientnet_b4_ham10k/      # EfficientNet-B4 × 5
├── efficientnet_b7_imagenet/    # EfficientNet-B7 × 5
├── transformers/                 # ViT × 5, Swin × 5
└── binary_classifiers/          # Binary classifiers × 15

Performance

Per-Class Performance

Model Architecture

7 Model Types (35 total models)

Ensemble Strategy

Stage 1: Base Ensemble (20 models)

• Average softmax probabilities from all base models

Stage 2: Binary Refinement (15 models) - Optional

• Apply binary classifiers to refine confusing pairs:
  • ADM ↔ Nevus
  • Nevus ↔ Melanoma
  • Solar Lentigo ↔ Ephelis

Usage

Installation

pip install torch torchvision timm pillow huggingface_hub

Download Models

from huggingface_hub import snapshot_download

# Option 1: Download base models only (20 models, ~5GB)
snapshot_download(
    repo_id="kindai-derma-ai/skin-lesion-ensemble",
    local_dir="./models",
    allow_patterns="base_models/**"
)

# Option 2: Download full ensemble (35 models, ~6.4GB)
snapshot_download(
    repo_id="kindai-derma-ai/skin-lesion-ensemble",
    local_dir="./models",
    ignore_patterns="base_models/**"  # Avoid duplicates
)

# Option 3: Download everything
snapshot_download(
    repo_id="kindai-derma-ai/skin-lesion-ensemble",
    local_dir="./models"
)

Inference with Base Models

import torch
import timm
from PIL import Image
from torchvision import transforms

# Load model (example with EfficientNet-B4)
model = timm.create_model('efficientnet_b4', pretrained=False, num_classes=8)
checkpoint = torch.load('models/base_models/efficientnet_b4_ham10k/efficientnet_b4_ham10k_fold0.pth')
model.load_state_dict(checkpoint['model_state_dict'])
model.eval()

# Preprocessing
transform = transforms.Compose([
    transforms.Resize((384, 384)),
    transforms.ToTensor(),
    transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225])
])

# Predict
image = Image.open('skin_lesion.jpg').convert('RGB')
input_tensor = transform(image).unsqueeze(0)

with torch.no_grad():
    output = model(input_tensor)
    probabilities = torch.softmax(output, dim=1)
    predicted_class = torch.argmax(probabilities, dim=1).item()

class_names = ['ADM', 'BCC', 'Ephelis', 'Melanoma', 'Melasma', 'Nevus', 'SK', 'Solar_Lentigo']
print(f"Prediction: {class_names[predicted_class]}")
print(f"Confidence: {probabilities[0][predicted_class]:.2%}")

File Structure

models/
├── base_models/                      # Base models only (20 models)
│   ├── efficientnet_b4_ham10k/
│   ├── efficientnet_b7_imagenet/
│   └── transformers/
│
├── efficientnet_b4_ham10k/           # Full ensemble components
├── efficientnet_b7_imagenet/
├── transformers/
└── binary_classifiers/               # Binary refinement (full ensemble only)

Training Details

• Input Size: 384 × 384
• Loss Function: Focal Loss (α=1, γ=2)
• Optimizer: AdamW (lr=1e-4, weight_decay=0.01)
• Scheduler: CosineAnnealingLR
• Batch Size: 16
• Epochs: 50 (with early stopping, patience=15)
• Cross-Validation: 5-fold stratified

Data Augmentation

• RandomHorizontalFlip
• RandomVerticalFlip
• RandomRotation (±20°)
• ColorJitter (brightness, contrast, saturation, hue)
• RandomAffine (translation, scale)

Limitations and Intended Use

Intended Use

• Research and educational purposes
• Dermatology AI research
• Clinical decision support (with physician oversight)

Limitations

• Not for clinical diagnosis: This model is intended as a decision support tool and should not replace professional medical diagnosis
• Dataset bias: Trained primarily on Japanese patient data; performance may vary on other populations
• Image quality dependency: Performance depends on image quality and standardized acquisition conditions

Out-of-Scope Use

• Autonomous clinical diagnosis without physician review
• Use on non-dermatoscopic images
• Commercial deployment without proper validation

Citation

@misc{skin_lesion_ensemble_2024,
  title={7-Model Ensemble for 8-Class Skin Lesion Classification},
  author={Kindai Dermatology AI Lab},
  year={2024},
  howpublished={\url{https://huggingface.co/kindai-derma-ai/skin-lesion-ensemble}}
}

License

This model is released under CC BY-NC 4.0.

• Academic/Research Use: Freely available
• Commercial Use: Contact authors for licensing

Acknowledgments

• HAM10000 Dataset
• timm library
• PyTorch

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Created: December 2024 Organization: Kindai Dermatology AI Lab