Data-Efficient Deep Learning Framework for Urolithiasis Detection Using Transfer and Self-Supervised Learning

The proposed framework demonstrates the feasibility of artificial intelligence-based urolithiasis detection in small-data clinical environments. Combining SSL and TL alleviates data scarcity and provides a foundation for developing generalizable and resource-efficient diagnostic models for urological imaging.