Multiclass Segmentation

Multiclass segmentation aims to partition an image into multiple distinct regions, each representing a different class or object, a crucial task in diverse fields like medical imaging and robotics. Current research emphasizes improving accuracy and robustness, particularly focusing on architectures like U-Net and Swin Transformers, often incorporating attention mechanisms and novel loss functions (e.g., Dice loss variations) to address challenges such as class imbalance and missing labels. These advancements are driving progress in applications ranging from automated medical diagnosis (e.g., dental and brain image analysis) to autonomous navigation, where reliable and uncertainty-aware segmentation is critical for safe operation.