On the relationship between calibrated predictors and unbiased volume
estimation

On the relationship between calibrated predictors and unbiased volume estimation

23 December 2021

Teodora Popordanoska

Dirk Vandermeulen

Matthew B. Blaschko

Papers citing "On the relationship between calibrated predictors and unbiased volume estimation"

12 / 12 papers shown

Title
Uncertainty Quantification for Machine Learning in Healthcare: A Survey L. J. L. Lopez Shaza Elsharief Dhiyaa Al Jorf Firas Darwish Congbo Ma Farah E. Shamout 101 0 0 04 May 2025
Beyond Classification: Definition and Density-based Estimation of Calibration in Object Detection Teodora Popordanoska A. Tiulpin Matthew B. Blaschko 30 8 0 11 Dec 2023
Marginal Thresholding in Noisy Image Segmentation M. Nordström Henrik Hult A. Maki NoLa 14 0 0 08 Apr 2023
Noisy Image Segmentation With Soft-Dice M. Nordström Henrik Hult A. Maki F. Löfman 31 2 0 03 Apr 2023
Dice Semimetric Losses: Optimizing the Dice Score with Soft Labels Zifu Wang Teodora Popordanoska J. Bertels Robin Lemmens Matthew B. Blaschko 24 10 0 28 Mar 2023
Jaccard Metric Losses: Optimizing the Jaccard Index with Soft Labels Zifu Wang Xuefei Ning Matthew B. Blaschko VLM 30 12 0 11 Feb 2023
Usable Region Estimate for Assessing Practical Usability of Medical Image Segmentation Models Yizhe Zhang Suraj Mishra Peixian Liang Hao Zheng D. Z. Chen 14 3 0 01 Jul 2022
Identifying and Combating Bias in Segmentation Networks by leveraging multiple resolutions Leonie Henschel David Kügler D. Andrews C. Nordahl M. Reuter 16 0 0 29 Jun 2022
RankSEG: A Consistent Ranking-based Framework for Segmentation Ben Dai Chunlin Li 34 1 0 27 Jun 2022
On Image Segmentation With Noisy Labels: Characterization and Volume Properties of the Optimal Solutions to Accuracy and Dice M. Nordström Henrik Hult J. Söderberg F. Löfman 16 6 0 13 Jun 2022
Simple and Scalable Predictive Uncertainty Estimation using Deep Ensembles Balaji Lakshminarayanan Alexander Pritzel Charles Blundell UQCV BDL 270 5,660 0 05 Dec 2016
Dropout as a Bayesian Approximation: Representing Model Uncertainty in Deep Learning Y. Gal Zoubin Ghahramani UQCV BDL 285 9,136 0 06 Jun 2015