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Comparative Benchmarking of Failure Detection Methods in Medical Image
  Segmentation: Unveiling the Role of Confidence Aggregation

Comparative Benchmarking of Failure Detection Methods in Medical Image Segmentation: Unveiling the Role of Confidence Aggregation

5 June 2024
M. Zenk
David Zimmerer
Fabian Isensee
Jeremias Traub
T. Norajitra
Paul F. Jäger
Klaus H. Maier-Hein
ArXivPDFHTML

Papers citing "Comparative Benchmarking of Failure Detection Methods in Medical Image Segmentation: Unveiling the Role of Confidence Aggregation"

4 / 4 papers shown
Title
Transformer-based out-of-distribution detection for clinically safe
  segmentation
Transformer-based out-of-distribution detection for clinically safe segmentation
M. Graham
Petru-Daniel Tudosiu
P. Wright
W. H. Pinaya
J. U-King-im
...
H. Jäger
D. Werring
P. Nachev
Sebastien Ourselin
M. Jorge Cardoso
MedIm
19
21
0
21 May 2022
TorchIO: A Python library for efficient loading, preprocessing,
  augmentation and patch-based sampling of medical images in deep learning
TorchIO: A Python library for efficient loading, preprocessing, augmentation and patch-based sampling of medical images in deep learning
Fernando Pérez-García
Rachel Sparks
Sébastien Ourselin
MedIm
LM&MA
135
426
0
09 Mar 2020
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
U-Net: Convolutional Networks for Biomedical Image Segmentation
U-Net: Convolutional Networks for Biomedical Image Segmentation
Olaf Ronneberger
Philipp Fischer
Thomas Brox
SSeg
3DV
232
75,770
0
18 May 2015
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