Deep Manifold Transformation for Explainable Visualization (DMTEV)
Date:
Presented cutting-edge research on deep manifold learning methods for high-dimensional data visualization. This talk covered the DMTEV framework, which combines topology-preserving dimensionality reduction with explainable AI techniques. Discussed applications in single-cell genomics, medical imaging, and complex data analysis, demonstrating how deep manifold transformation enables interpretable visualization of complex biological and clinical datasets while preserving intrinsic data structures.