Deep Bayesian Segmentation

Deep Bayesian segmentation uses probabilistic deep learning models to improve the accuracy and reliability of image segmentation tasks, addressing limitations of traditional deterministic approaches by providing uncertainty quantification alongside predictions. Current research focuses on applying Bayesian methods to various architectures like UNet and FPN, incorporating techniques such as Monte Carlo dropout and normalized flows to achieve better calibration and robustness, particularly in medical imaging applications like polyp and eosinophil detection. This approach is significant because it enhances the trustworthiness of automated segmentation, aiding in clinical decision-making and improving the objectivity of analyses in diverse fields ranging from medical diagnosis to historical artifact analysis.