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Fooling the Crowd with Deep Learning-based Methods

Fooling the Crowd with Deep Learning-based Methods

30 November 2019
Christian Marzahl
Marc Aubreville
C. Bertram
Stefan Gerlach
Jennifer K. Maier
J. Voigt
Jenny Hill
R. Klopfleisch
Andreas Maier
ArXiv (abs)PDFHTML

Papers citing "Fooling the Crowd with Deep Learning-based Methods"

3 / 3 papers shown
Title
NuCLS: A scalable crowdsourcing, deep learning approach and dataset for
  nucleus classification, localization and segmentation
NuCLS: A scalable crowdsourcing, deep learning approach and dataset for nucleus classification, localization and segmentation
M. Amgad
Lamees A. Atteya
Hagar Hussein
K. Mohammed
Ehab Hafiz
...
Critical Care
David Manthey
Atlanta
D. Neurology
Lurie Cancer Center
87
75
0
18 Feb 2021
Are fast labeling methods reliable? A case study of computer-aided
  expert annotations on microscopy slides
Are fast labeling methods reliable? A case study of computer-aided expert annotations on microscopy slides
Christian Marzahl
C. Bertram
Marc Aubreville
Anne Petrick
Kristina Weiler
...
Alina Langenhagen
A. Jasensky
J. Voigt
R. Klopfleisch
Andreas Maier
51
16
0
13 Apr 2020
Deep neural network models for computational histopathology: A survey
Deep neural network models for computational histopathology: A survey
C. Srinidhi
Ozan Ciga
Anne L. Martel
AI4CE
177
584
0
28 Dec 2019
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