Multi Modality Fusion

Multi-modality fusion integrates data from diverse sources (e.g., images, clinical records, sensor readings) to improve the accuracy and robustness of machine learning models. Current research emphasizes developing effective fusion architectures, often employing deep learning models like transformers and convolutional neural networks, to address challenges such as missing data, modality inconsistencies, and high dimensionality. This approach holds significant promise for advancing various fields, including medical diagnosis (e.g., cancer classification, brain tumor segmentation), autonomous driving (e.g., object detection, scene understanding), and remote sensing (e.g., satellite image classification), by leveraging the complementary information provided by multiple data modalities.