Incorporating Contextual Cues for Image Recognition: A Multi-Modal Semantic Fusion Model Sensitive to Key Information

Multi-modal data feature fusion can effectively improve the accuracy of primary modal pattern recognition and address the issue of missing data through multi-modal collaboration. To some extent, supplementing multi-view information can alleviate the conflict between medical images’ reliance on professional annotations and the limited number of annotations. However, there are still several challenges in engineering multimodal feature perception and fusion for medical images, including imbalanced patterns that make it challenging to establish a weighted allocation criterion suitable for different tasks, misleading factors not positively correlated with knowledge richness, and complexities associated with integrating modalities having diverse formats for text and time series data into overall feature engineering and similarity. This paper introduces a multi-modal semantic fusion model that is sensitive to key information represented by contextual cues. Furthermore, it incorporates a dynamic feature engineering scheme capable of acquiring and integrating important insights from three perspectives with an optimized structure. Additionally, it presents a novel similarity evaluation criteria and fusion mode that adaptively adjust weights based on their contributions, facilitating the measurement of target object category likelihoods across various modalities for weighted analyses. The system combines LSTM and CNN models to builda sequence and local information sensitive structure designed to integrate heterogeneous features while embedding the multimodal similarity fusion. Emphasizing small yet compelling pieces of information such as contextual cues that prompt medical representation assignment to the target category, our system places significant importance on specificity removal and accuracy enhancement, exemplified by introducing contextual information demonstrating our sensitivity to contextual cues despite our non-specifically structured approach.