Abstract
INTRODUCTION: The integration of pathology and radiology through artificial intelligence (AI) represents a groundbreaking advancement in medical imaging, providing a powerful tool for accurate diagnostics and the optimization of clinical workflows. Traditional image classification methods encounter substantial challenges due to the inherent complexity and heterogeneity of medical imaging datasets, which include multi-modal data sources, imbalanced class distributions, and the critical need for interpretability in clinical decision-making. METHODS: Addressing these limitations, this study introduces an innovative deep learning-based framework tailored for AI-assisted medical imaging tasks. It incorporates two novel components: the Adaptive Multi-Resolution Imaging Network (AMRI-Net) and the Explainable Domain-Adaptive Learning (EDAL) strategy. AMRI-Net enhances diagnostic accuracy by leveraging multi-resolution feature extraction, attention-guided fusion mechanisms, and task-specific decoders, allowing the model to accurately identify both detailed and overarching patterns across various imaging techniques, such as X-rays, CT, and MRI scans. EDAL significantly improves domain generalizability through advanced domain alignment techniques while integrating uncertainty-aware learning to prioritize high-confidence predictions. It employs attention-based interpretability tools to highlight critical image regions, improving transparency and clinical trust in AI-driven diagnoses. RESULTS: Experimental results on multi-modal medical imaging datasets underscore the framework's superior performance, with classification accuracies reaching up to 94.95% and F1-Scores up to 94.85%, thereby enhancing transparency and clinical trust in AI-driven diagnoses. DISCUSSION: This research bridges the gap between pathology and radiology, offering a comprehensive AI-driven solution that aligns with the evolving demands of modern healthcare by ensuring precision, reliability, and interpretability in medical imaging.