Evidential Segmentation

Evidential segmentation is a field focusing on improving the reliability and robustness of segmentation models, particularly in applications like medical image analysis and object tracking, by explicitly quantifying uncertainty in predictions. Current research emphasizes incorporating uncertainty measures, often using belief function theory or probabilistic frameworks like the normal inverse gamma distribution, into deep learning architectures to create more confident and reliable segmentations. This approach leads to improved accuracy, especially in handling noisy data, missing modalities, and out-of-distribution samples, with significant implications for applications requiring high-stakes decision-making based on image analysis.