\section{Conclusion}
We presented the Statistically Coherent Network (SCN), a multi-modal deep learning framework that fuses DICOM images, radiomics, and patient metadata into a structured multi-space latent representation. By jointly optimizing a T-stochastic loss for distributional alignment and a triplet loss for geometric separation, SCN learns interpretable and statistically coherent embeddings. Experimental results across four diverse clinical tasks demonstrate the superiority of SCN in classification performance and latent consistency, validating the effectiveness of integrating modality-specific features with statistically grounded objectives for robust and generalizable medical AI.
