Clinical Scorecard: Privacy-Conscious Skin Cancer Diagnosis through Federated Learning and Deep Neural Networks

At a Glance

Key Highlights

• Federated Learning enables privacy-sensitive skin cancer classification.
• MobileNetV2 achieved 98.88% accuracy, outperforming centralized models.
• Decentralized training reduces communication overhead and enhances performance.
• Framework addresses institutional heterogeneity in clinical data.
• Supports multimodal data integration for improved diagnostic fidelity.

Guideline-Based Recommendations

Diagnosis

• Utilize federated learning models for skin cancer classification.
• Incorporate multimodal data for enhanced diagnostic accuracy.

Management

• Implement privacy-preserving AI solutions in dermatology.
• Encourage collaboration among institutions without data sharing.

Monitoring & Follow-up

• Regularly evaluate model performance across different clinical settings.
• Monitor the impact of non-IID data distributions on model accuracy.

Risks

• Potential for misclassification due to reliance on decentralized data.
• Challenges in maintaining model performance with increasing non-IID severity.

Patient & Prescribing Data

Patients with suspected skin cancer, especially melanoma.

AI-aided diagnosis can lead to earlier detection and personalized treatment plans.

Clinical Best Practices

• Adopt federated learning frameworks to enhance patient privacy.
• Utilize lightweight architectures like MobileNetV2 for efficient model training.
• Encourage the integration of diverse data types for comprehensive diagnostics.

References

• Federated Learning in Medical Imaging
• Skin Cancer Epidemiology