Investigating and Improving Latent Density Segmentation Models for Aleatoric Uncertainty Quantification in Medical Imaging

31 July 2023

Papers citing "Investigating and Improving Latent Density Segmentation Models for Aleatoric Uncertainty Quantification in Medical Imaging"

6 / 6 papers shown

Title
A Review of Bayesian Uncertainty Quantification in Deep Probabilistic Image Segmentation M. Valiuddin R. V. Sloun C.G.A. Viviers Peter H. N. de With Fons van der Sommen UQCV 79 1 0 25 Nov 2024
Segmentation-based Assessment of Tumor-Vessel Involvement for Surgical Resectability Prediction of Pancreatic Ductal Adenocarcinoma Christiaan G. A. Viviers Mark Ramaekers Amaan Valiuddin Terese A. E. Hellström Nick Tasios ... Lotte J. S. Ewals Joost Nederend Peter H. N. de With M. Luyer Fons van der Sommen 22 3 0 01 Oct 2023
Probabilistic 3D segmentation for aleatoric uncertainty quantification in full 3D medical data Christiaan G. A. Viviers Amaan Valiuddin Peter H. N. de With Fons van der Sommen 28 5 0 01 May 2023
Stochastic Segmentation with Conditional Categorical Diffusion Models L. Zbinden Lars Doorenbos Theodoros Pissas Adrian Thomas Huber Raphael Sznitman Pablo Márquez-Neila DiffM 32 30 0 15 Mar 2023
MedSegDiff: Medical Image Segmentation with Diffusion Probabilistic Model Junde Wu Rao Fu Huihui Fang Yu Zhang Yehui Yang Haoyi Xiong Huiying Liu Yanwu Xu MedIm VLM DiffM 98 238 0 01 Nov 2022
Simple and Scalable Predictive Uncertainty Estimation using Deep Ensembles Balaji Lakshminarayanan Alexander Pritzel Charles Blundell UQCV BDL 268 5,652 0 05 Dec 2016