Maximum Entropy on Erroneous Predictions (MEEP): Improving model
calibration for medical image segmentation

Maximum Entropy on Erroneous Predictions (MEEP): Improving model calibration for medical image segmentation

22 December 2021

Agostina J. Larrazabal

Cesar E. Martínez

Jose Dolz

Papers citing "Maximum Entropy on Erroneous Predictions (MEEP): Improving model calibration for medical image segmentation"

7 / 7 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 98 0 0 04 May 2025
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 89 1 0 25 Nov 2024
Do not trust what you trust: Miscalibration in Semi-supervised Learning Shambhavi Mishra Balamurali Murugesan Ismail Ben Ayed M. Pedersoli Jose Dolz 43 2 0 22 Mar 2024
Class and Region-Adaptive Constraints for Network Calibration Balamurali Murugesan Julio Silva-Rodríguez Ismail Ben Ayed Jose Dolz 32 1 0 19 Mar 2024
Boundary-weighted logit consistency improves calibration of segmentation networks Neerav Karani Neel Dey Polina Golland 17 3 0 16 Jul 2023
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 264 9,136 0 06 Jun 2015