Skin Cancer Detection App
we've used python and Swin Transformers can be production-ready for dermatology through three innovations - MC Dropout for confidence scoring, triple-strategy imbalance handling for equitable cancer detection, and memory optimizations enabling training on consumer hardware. The system achieves 88% accuracy with 98% accuracy on confident cases, making it suitable for clinical triage workflows. This work transforms a research model into a practical tool that knows its limitations and works within computational constraints of real healthcare settings.
Full Description
we've used python and Swin Transformers can be production-ready for dermatology through three innovations - MC Dropout for confidence scoring, triple-strategy imbalance handling for equitable cancer detection, and memory optimizations enabling training on consumer hardware. The system achieves 88% accuracy with 98% accuracy on confident cases, making it suitable for clinical triage workflows. This work transforms a research model into a practical tool that knows its limitations and works within computational constraints of real healthcare settings.