Interpretable Segmentation

Interpretable segmentation aims to improve the transparency and understandability of image segmentation models, addressing the "black box" problem inherent in many deep learning approaches. Current research focuses on developing methods that provide insights into model decisions, employing techniques like prototype-based learning, counterfactual explanations, and uncertainty quantification within architectures such as U-Nets and variational autoencoders. This increased interpretability is crucial for building trust in AI systems, particularly in high-stakes applications like medical image analysis and earth observation, where understanding the reasoning behind predictions is paramount for reliable decision-making. The development of more interpretable models also facilitates better model debugging and refinement.