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Improving Calibration and Out-of-Distribution Detection in Medical Image
  Segmentation with Convolutional Neural Networks

Improving Calibration and Out-of-Distribution Detection in Medical Image Segmentation with Convolutional Neural Networks

12 April 2020
Davood Karimi
Ali Gholipour
    OOD
ArXivPDFHTML

Papers citing "Improving Calibration and Out-of-Distribution Detection in Medical Image Segmentation with Convolutional Neural Networks"

4 / 4 papers shown
Title
A Stitch in Time Saves Nine: A Train-Time Regularizing Loss for Improved
  Neural Network Calibration
A Stitch in Time Saves Nine: A Train-Time Regularizing Loss for Improved Neural Network Calibration
R. Hebbalaguppe
Jatin Prakash
Neelabh Madan
Chetan Arora
UQCV
25
42
0
25 Mar 2022
Confidence Calibration for Object Detection and Segmentation
Confidence Calibration for Object Detection and Segmentation
Fabian Küppers
Anselm Haselhoff
Jan Kronenberger
Jonas Schneider
UQCV
25
4
0
25 Feb 2022
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
Agostina J. Larrazabal
Cesar E. Martínez
Jose Dolz
Enzo Ferrante
19
15
0
22 Dec 2021
Simple and Scalable Predictive Uncertainty Estimation using Deep
  Ensembles
Simple and Scalable Predictive Uncertainty Estimation using Deep Ensembles
Balaji Lakshminarayanan
Alexander Pritzel
Charles Blundell
UQCV
BDL
276
5,675
0
05 Dec 2016
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