Bridging Multi-Modal Radiomics and Deep Learning Features for Precise Lesion Detection using Uncertainty-Aware Cross-Attention Fusion Networks

Abstract

The integration of disparate data streams in medical imaging represents a significant frontier in precision oncology and diagnostic radiology. While radiomics provides high-dimensional, engineered features that capture textural and morphological nuances, deep learning offers automated, latent feature extraction capable of identifying complex non-linear patterns. This paper investigates a systemic framework for bridging these two methodologies through the implementation of Uncertainty-Aware Cross-Attention Fusion Networks. We propose a multi-modal architecture that utilizes cross-attention mechanisms to dynamically weigh the contributions of radiomic descriptors and deep-learned representations, supplemented by an uncertainty estimation layer that quantifies the reliability of the fusion process. Beyond the algorithmic architecture, the study provides an exhaustive system-level analysis of the structural trade-offs inherent in multi-modal fusion, the infrastructure required for large-scale clinical deployment, and the governance frameworks necessary to ensure algorithmic fairness and robustness. We further discuss the socio-technical implications of deploying uncertainty-aware systems, emphasizing how transparency in model confidence can influence clinical decision-making and policy development. By examining the intersection of engineering complexity and clinical utility, this research outlines a sustainable path for the integration of hybrid diagnostic systems into the modern healthcare ecosystem, ensuring that precision lesion detection is both technically rigorous and ethically grounded.