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2012.06436
Cited By
Uncertainty-driven refinement of tumor-core segmentation using 3D-to-2D networks with label uncertainty
11 December 2020
Richard McKinley
M. Rebsamen
Katrin Daetwyler
Raphael Meier
Piotr Radojewski
Roland Wiest
3DV
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Papers citing
"Uncertainty-driven refinement of tumor-core segmentation using 3D-to-2D networks with label uncertainty"
4 / 4 papers shown
Panoptica -- instance-wise evaluation of 3D semantic and instance segmentation maps
Florian Kofler
Hendrik Möller
Josef A. Buchner
Ezequiel de la Rosa
Ivan Ezhov
...
Stefan K. Ehrlich
Annika Reinke
Bjoern Menze
Benedikt Wiestler
Marie Piraud
ISeg
220
8
0
05 Dec 2023
Glioblastoma Tumor Segmentation using an Ensemble of Vision Transformers
Huafeng Liu
Benjamin Dowdell
Todd Engelder
Zarah Pulmano
Nicolas Osa
Arko Barman
MedIm
115
1
0
09 Nov 2023
Trustworthy clinical AI solutions: a unified review of uncertainty quantification in deep learning models for medical image analysis
Benjamin Lambert
Florence Forbes
A. Tucholka
Senan Doyle
Harmonie Dehaene
M. Dojat
295
184
0
05 Oct 2022
QU-BraTS: MICCAI BraTS 2020 Challenge on Quantifying Uncertainty in Brain Tumor Segmentation - Analysis of Ranking Scores and Benchmarking Results
Raghav Mehta
Angelos Filos
Ujjwal Baid
C. Sako
Richard McKinley
...
Christos Davatzikos
Bjoern Menze
Spyridon Bakas
Y. Gal
Tal Arbel
UQCV
300
69
0
19 Dec 2021
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