Multi-omics approaches for image classification in disease diagnosis

Traditional image classification approaches in disease contexts-such as those relying solely on histopathological features or genomic imaging-tend to overlook the broader ecological and systemic dimensions that are essential for decoding the mechanisms of microbial pathogenesis. These single-modal methods often suffer from significant limitations, including reduced scalability to diverse clinical settings, poor generalizability across patient populations, and an inability to handle partially observed or biologically variable data. Such constraints diminish their effectiveness in precision diagnostics, disease subtyping, and therapeutic decision-making. By contrast, our approach emphasizes multi-modal integration and model interpretability, aiming to overcome these limitations and advance the development of next-generation diagnostic tools that are both clinically actionable and biologically grounded.