Dseg Lime

DSEG-LIME is a method for improving the interpretability of complex machine learning models, particularly in image analysis, by enhancing the quality of local explanations. Current research focuses on refining the underlying segmentation techniques used in LIME, addressing issues like instability and ensuring alignment with human understanding of features, often through hierarchical or data-driven approaches. This work is significant because reliable and accurate explanations are crucial for building trust in AI systems, especially in high-stakes applications like healthcare, and for improving model development by identifying biases or weaknesses.