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Failure Detection in Medical Image Classification: A Reality Check and
  Benchmarking Testbed

Failure Detection in Medical Image Classification: A Reality Check and Benchmarking Testbed

27 May 2022
Mélanie Bernhardt
Fabio De Sousa Ribeiro
Ben Glocker
ArXivPDFHTML

Papers citing "Failure Detection in Medical Image Classification: A Reality Check and Benchmarking Testbed"

5 / 5 papers shown
Title
Calibration and Uncertainty for multiRater Volume Assessment in multiorgan Segmentation (CURVAS) challenge results
Calibration and Uncertainty for multiRater Volume Assessment in multiorgan Segmentation (CURVAS) challenge results
Meritxell Riera-Marin
S. Ko
Julia Rodriguez-Comas
Matthias Stefan May
Zhaohong Pan
...
Anton Aubanell
Andreu Antolin
Javier Garcia-Lopez
M. A. G. Ballester
Adrian Galdran
UQCV
36
0
0
13 May 2025
A Call to Reflect on Evaluation Practices for Failure Detection in Image
  Classification
A Call to Reflect on Evaluation Practices for Failure Detection in Image Classification
Paul F. Jaeger
Carsten T. Lüth
Lukas Klein
Till J. Bungert
UQCV
11
35
0
28 Nov 2022
MedMNIST v2 -- A large-scale lightweight benchmark for 2D and 3D
  biomedical image classification
MedMNIST v2 -- A large-scale lightweight benchmark for 2D and 3D biomedical image classification
Jiancheng Yang
Rui Shi
D. Wei
Zequan Liu
Lin Zhao
B. Ke
Hanspeter Pfister
Bingbing Ni
VLM
161
645
0
27 Oct 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
268
5,652
0
05 Dec 2016
Dropout as a Bayesian Approximation: Representing Model Uncertainty in
  Deep Learning
Dropout as a Bayesian Approximation: Representing Model Uncertainty in Deep Learning
Y. Gal
Zoubin Ghahramani
UQCV
BDL
247
9,109
0
06 Jun 2015
1