5 papers
Flow-Based Generative Modeling for Optimizing Sampling Policies in Compressed Sensing Applications
Roman Pavelkin, Luis A. Zavala-Mondragon, Christiaan G. A. Viviers +1
Numerous modern applications in signal processing and medical imaging necessitate acquiring high-dimensional signals under tight resource constraints. Traditional sampling theory s…
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…
Probabilistic 3D segmentation for aleatoric uncertainty quantification in full 3D medical data
Christiaan G. A. Viviers, Amaan M. M. Valiuddin, Peter H. N. de With +1
Uncertainty quantification in medical images has become an essential addition to segmentation models for practical application in the real world. Although there are valuable develo…
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…
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…