Multimodal Segmentation

Multimodal segmentation aims to improve the accuracy and robustness of image segmentation by integrating information from multiple data sources (e.g., different imaging modalities, audio, text). Current research focuses on developing effective fusion strategies for combining these diverse data types, employing architectures like transformers and U-Nets, and addressing challenges such as data scarcity and modality misalignment through techniques like semi-supervised learning and consistency embedding. This field is significantly impacting medical image analysis, enabling more accurate diagnoses and treatment planning, and also finds applications in other areas such as speech processing and remote sensing, where multimodal data is readily available.