System Architecture¶
Overview¶
This document outlines the technical architecture of the fairness-aware skin cancer detection system.
Components¶
1. Data Pipeline¶
- Dataset loaders for HAM10000, ISIC, DDI
- Preprocessing and normalization
- Augmentation strategies
- Balanced sampling for fairness
2. Model Architecture¶
- Base models: ConvNeXt, Swin Transformer, Vision Transformer
- Multi-task learning heads
- Fairness-aware components
3. Training Pipeline¶
- Loss functions (cross-entropy, focal loss, fairness regularization)
- Optimization strategies
- Adversarial debiasing
- Progressive training protocols
4. Evaluation Framework¶
- Standard metrics: Accuracy, Precision, Recall, F1, AUC-ROC
- Fairness metrics: Demographic Parity, Equalized Odds, Calibration
- Subgroup analysis by skin tone (Fitzpatrick scale)
Technology Stack¶
- Framework: PyTorch 2.0+
- Vision Models: timm (PyTorch Image Models)
- Fairness: fairlearn, AIF360
- Experiment Tracking: TensorBoard, Weights & Biases
- Configuration: Hydra
Design Principles¶
- Modularity: Plug-and-play components
- Reproducibility: Configuration-driven experiments
- Scalability: Efficient data loading and distributed training
- Observability: Comprehensive logging and metrics
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