3 papers
cs.CV2024
A Review of Bayesian Uncertainty Quantification in Deep Probabilistic Image Segmentation
M. M. A. Valiuddin, R. J. G. van Sloun, C. G. A. Viviers +2
Advances in architectural design, data availability, and compute have driven remarkable progress in semantic segmentation. Yet, these models often rely on relaxed Bayesian assumpti…
cs.CV2021
Improving Aleatoric Uncertainty Quantification in Multi-Annotated Medical Image Segmentation with Normalizing Flows
M. M. A. Valiuddin, C. G. A. Viviers, R. J. G. van Sloun +2
Quantifying uncertainty in medical image segmentation applications is essential, as it is often connected to vital decision-making. Compelling attempts have been made in quantifyin…
cs.CV2021
Out-of-Distribution Detection of Melanoma using Normalizing Flows
M. M. A. Valiuddin, C. G. A. Viviers
Generative modelling has been a topic at the forefront of machine learning research for a substantial amount of time. With the recent success in the field of machine learning, espe…