Geert Litjens
Geert Litjens
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Artificial intelligence for diagnosis and Gleason grading of prostate cancer: the PANDA challenge
Wouter Bulten
,
Kimmo Kartasalo
,
Po-Hsuan Cameron Chen
,
Peter Ström
,
Hans Pinckaers
,
Kunal Nagpal
,
Yuannan Cai
,
David F. Steiner
,
Hester van Boven
,
Robert Vink
,
Christina Hulsbergen-van de Kaa
,
Jeroen van der Laak
,
Mahul B. Amin
,
Andrew J. Evans
,
Theodorus van der Kwast
,
Robert Allan
,
Peter A. Humphrey
,
Henrik Grönberg
,
Hemamali Samaratunga
,
Brett Delahunt
,
Toyonori Tsuzuki
,
Tomi Häkkinen
,
Lars Egevad
,
Maggie Demkin
,
Sohier Dane
,
Fraser Tan
,
Masi Valkonen
,
Greg S. Corrado
,
Lily Peng
,
Craig H. Mermel
,
Pekka Ruusuvuori
,
Geert Litjens
,
Martin Eklund
,
Américo Brilhante
,
Aslı Çakır
,
Xavier Farré
,
Katerina Geronatsiou
,
Vincent Molinié
,
Guilherme Pereira
,
Paromita Roy
,
Günter Saile
,
Paulo G. O. Salles
,
Ewout Schaafsma
,
Joëlle Tschui
,
Jorge Billoch-Lima
,
Emíio M. Pereira
,
Ming Zhou
,
Shujun He
,
Sejun Song
,
Qing Sun
,
Hiroshi Yoshihara
,
Taiki Yamaguchi
,
Kosaku Ono
,
Tao Shen
,
Jianyi Ji
,
Arnaud Roussel
,
Kairong Zhou
,
Tianrui Chai
,
Nina Weng
,
Dmitry Grechka
,
Maxim V. Shugaev
,
Raphael Kiminya
,
Vassili Kovalev
,
Dmitry Voynov
,
Valery Malyshev
,
Elizabeth Lapo
,
Manuel Campos
,
Noriaki Ota
,
Shinsuke Yamaoka
,
Yusuke Fujimoto
,
Kentaro Yoshioka
,
Joni Juvonen
,
Mikko Tukiainen
,
Antti Karlsson
,
Rui Guo
,
Chia-Lun Hsieh
,
Igor Zubarev
,
Habib S. T. Bukhar
,
Wenyuan Li
,
Jiayun Li
,
William Speier
,
Corey Arnold
,
Kyungdoc Kim
,
Byeonguk Bae
,
Yeong Won Kim
,
Hong-Seok Lee
,
Jeonghyuk Park
,
the PANDA challenge consortium
Using deep learning for quantification of cellularity and cell lineages in bone marrow biopsies and comparison to normal age-related variation.
Leander van Eekelen
,
Hans Pinckaers
,
Michiel van den Brand
,
Konnie M. Hebeda
,
Geert Litjens
Artificial Intelligence for Diagnosis and Gleason Grading of Prostate Cancer in Biopsies-Current Status and Next Steps.
Kimmo Kartasalo
,
Wouter Bulten
,
Brett Delahunt
,
Po-Hsuan Cameron Chen
,
Hans Pinckaers
,
Henrik Olsson
,
Xiaoyi Ji
,
Nita Mulliqi
,
Hemamali Samaratunga
,
Toyonori Tsuzuki
,
Johan Lindberg
,
Mattias Rantalainen
,
Carolina Wählby
,
Geert Litjens
,
Pekka Ruusuvuori
,
Lars Egevad
,
Martin Eklund
Mini Review: The Last Mile-Opportunities and Challenges for Machine Learning in Digital Toxicologic Pathology.
O. Turner
,
B. Knight
,
A. Zuraw
,
G. Litjens
,
D. Rudmann
Deep learning in histopathology: the path to the clinic.
J. van der Laak
,
G. Litjens
,
F. Ciompi
Residual cyclegan for robust domain transformation of histopathological tissue slides.
T. de Bel
,
J. Bokhorst
,
J. van der Laak
,
G. Litjens
Optimized tumour infiltrating lymphocyte assessment for triple negative breast cancer prognostics.
M. Balkenhol
,
F. Ciompi
,
Ż. Świderska-Chadaj
,
R. van de Loo
,
M. Intezar
,
I. Otte-Höller
,
D. Geijs
,
J. Lotz
,
N. Weiss
,
T. de Bel
,
G. Litjens
,
P. Bult
,
J. van der Laak
Detection of prostate cancer in whole-slide images through end-to-end training with image-level labels.
H. Pinckaers
,
W. Bulten
,
J. van der Laak
,
G. Litjens
Neural Image Compression for Gigapixel Histopathology Image Analysis.
D. Tellez
,
G. Litjens
,
J. van der Laak
,
F. Ciompi
Impact of rescanning and normalization on convolutional neural network performance in multi-center, whole-slide classification of prostate cancer
Ż. Świderska-Chadaj
,
T. de Bel
,
L. Blanchet
,
A. Baidoshvili
,
D. Vossen
,
J. van der Laak
,
G. Litjens
»
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