Segmentation Based Approach

Segmentation-based approaches aim to partition images into meaningful regions, facilitating analysis and interpretation across diverse fields. Current research emphasizes the development and application of advanced deep learning architectures, including U-Net variants, transformers (like Mamba), and foundation models (like SAM), often combined with innovative loss functions and data augmentation techniques to address challenges such as class imbalance and limited annotated data. These methods are proving impactful in various applications, from medical image analysis (e.g., tumor detection, organ segmentation) and remote sensing (e.g., crop field mapping, flood detection) to other domains requiring precise object delineation. The ongoing focus is on improving accuracy, efficiency, and explainability, particularly in scenarios with scarce or heterogeneous data.