ResearchTrend.AI
  • Papers
  • Communities
  • Events
  • Blog
  • Pricing
Papers
Communities
Social Events
Terms and Conditions
Pricing
Parameter LabParameter LabTwitterGitHubLinkedInBlueskyYoutube

© 2025 ResearchTrend.AI, All rights reserved.

  1. Home
  2. Papers
  3. 1807.08284
  4. Cited By
Predicting breast tumor proliferation from whole-slide images: the
  TUPAC16 challenge
v1v2 (latest)

Predicting breast tumor proliferation from whole-slide images: the TUPAC16 challenge

22 July 2018
M. Veta
Y. Heng
N. Stathonikos
B. Bejnordi
F. Beca
Thomas Wollmann
K. Rohr
Manan A. Shah
Dayong Wang
Mikaël Rousson
Martin Hedlund
David Tellez
F. Ciompi
Erwan Zerhouni
D. Lanyi
Matheus Palhares Viana
V. Kovalev
V. Liauchuk
H. A. Phoulady
Talha Qaiser
S. Graham
Nasir M. Rajpoot
E. Sjöblom
J. Molin
K. Paeng
Sangheum Hwang
Sunggyun Park
Zhipeng Jia
E. Chang
Yan Xu
Andrew H. Beck
P. Diest
J. Pluim
ArXiv (abs)PDFHTML

Papers citing "Predicting breast tumor proliferation from whole-slide images: the TUPAC16 challenge"

10 / 10 papers shown
Title
HISTAI: An Open-Source, Large-Scale Whole Slide Image Dataset for Computational Pathology
HISTAI: An Open-Source, Large-Scale Whole Slide Image Dataset for Computational Pathology
Dmitry Nechaev
Alexey Pchelnikov
Ekaterina Ivanova
LM&MAVLM
28
0
0
17 May 2025
HRScene: How Far Are VLMs from Effective High-Resolution Image Understanding?
HRScene: How Far Are VLMs from Effective High-Resolution Image Understanding?
Yusen Zhang
Wenliang Zheng
Aashrith Madasu
Peng Shi
Ryo Kamoi
...
Ranran Haoran Zhang
Avitej Iyer
Renze Lou
Wenpeng Yin
Rui Zhang
286
0
0
25 Apr 2025
Whole-Slide Mitosis Detection in H&E Breast Histology Using PHH3 as a
  Reference to Train Distilled Stain-Invariant Convolutional Networks
Whole-Slide Mitosis Detection in H&E Breast Histology Using PHH3 as a Reference to Train Distilled Stain-Invariant Convolutional Networks
David Tellez
M. Balkenhol
I. Otte-Höller
Rob van de Loo
R. Vogels
...
S. Mol
N. Karssemeijer
G. Litjens
J. A. van der Laak
F. Ciompi
60
245
0
17 Aug 2018
Why rankings of biomedical image analysis competitions should be
  interpreted with care
Why rankings of biomedical image analysis competitions should be interpreted with care
Lena Maier-Hein
Matthias Eisenmann
Annika Reinke
Sinan Onogur
Marko Stankovic
...
Ching-Wei Wang
M. Weber
G. Zheng
Pierre Jannin
A. Kopp-Schneider
54
302
0
06 Jun 2018
Wide Residual Networks
Wide Residual Networks
Sergey Zagoruyko
N. Komodakis
351
8,000
0
23 May 2016
Deep Residual Learning for Image Recognition
Deep Residual Learning for Image Recognition
Kaiming He
Xinming Zhang
Shaoqing Ren
Jian Sun
MedIm
2.2K
194,426
0
10 Dec 2015
U-Net: Convolutional Networks for Biomedical Image Segmentation
U-Net: Convolutional Networks for Biomedical Image Segmentation
Olaf Ronneberger
Philipp Fischer
Thomas Brox
SSeg3DV
1.9K
77,378
0
18 May 2015
Assessment of algorithms for mitosis detection in breast cancer
  histopathology images
Assessment of algorithms for mitosis detection in breast cancer histopathology images
M. Veta
P. Diest
S. Willems
Haibo Wang
A. Madabhushi
...
Nasir M. Rajpoot
E. Arkoumani
M. Laclé
M. Viergever
J. Pluim
72
431
0
21 Nov 2014
Going Deeper with Convolutions
Going Deeper with Convolutions
Christian Szegedy
Wei Liu
Yangqing Jia
P. Sermanet
Scott E. Reed
Dragomir Anguelov
D. Erhan
Vincent Vanhoucke
Andrew Rabinovich
485
43,694
0
17 Sep 2014
Very Deep Convolutional Networks for Large-Scale Image Recognition
Very Deep Convolutional Networks for Large-Scale Image Recognition
Karen Simonyan
Andrew Zisserman
FAttMDE
1.7K
100,508
0
04 Sep 2014
1