Reza Khoshkangini

Blasto-Net achieves remarkable accuracy in blastocyst analysis, outperforming traditional methods while providing interpretable results that can directly influence IVF outcomes.

Attention-guided deep learning can achieve over 90% accuracy in sperm morphology classification while providing critical interpretability for clinical applications.

Context-aware features can slash IVF prediction errors by over 60%, revealing critical insights hidden in environmental data.