O
Oscar Geessink
Researcher at Radboud University Nijmegen
Publications - 10
Citations - 3109
Oscar Geessink is an academic researcher from Radboud University Nijmegen. The author has contributed to research in topics: H&E stain & Colorectal cancer. The author has an hindex of 8, co-authored 9 publications receiving 1897 citations. Previous affiliations of Oscar Geessink include Medisch Spectrum Twente.
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Journal ArticleDOI
Diagnostic Assessment of Deep Learning Algorithms for Detection of Lymph Node Metastases in Women With Breast Cancer.
Babak Ehteshami Bejnordi,Mitko Veta,Paul J. van Diest,Bram van Ginneken,Nico Karssemeijer,Geert Litjens,Jeroen van der Laak,Meyke Hermsen,Quirine F. Manson,Maschenka Balkenhol,Oscar Geessink,N. Stathonikos,Marcory C. R. F. van Dijk,Peter Bult,Francisco Beca,Andrew H. Beck,Dayong Wang,Aditya Khosla,Rishab Gargeya,Humayun Irshad,Aoxiao Zhong,Qi Dou,Qi Dou,Quanzheng Li,Hao Chen,Huangjing Lin,Pheng-Ann Heng,Christian Haß,Elia Bruni,Quincy Wong,Ugur Halici,Mustafa Umit Oner,Rengul Cetin-Atalay,Matt Berseth,Vitali Khvatkov,Alexei Vylegzhanin,Oren Kraus,Muhammad Shaban,Nasir M. Rajpoot,Nasir M. Rajpoot,Ruqayya Awan,Korsuk Sirinukunwattana,Talha Qaiser,Yee-Wah Tsang,David Tellez,Jonas Annuscheit,Peter Hufnagl,Mira Valkonen,Kimmo Kartasalo,Kimmo Kartasalo,Leena Latonen,Pekka Ruusuvuori,Pekka Ruusuvuori,Kaisa Liimatainen,Shadi Albarqouni,Bharti Mungal,Ami George,Stefanie Demirci,Nassir Navab,Seiryo Watanabe,Shigeto Seno,Yoichi Takenaka,Hideo Matsuda,Hady Ahmady Phoulady,Vassili Kovalev,A. Kalinovsky,Vitali Liauchuk,Gloria Bueno,M. Milagro Fernández-Carrobles,Ismael Serrano,Oscar Deniz,Daniel Racoceanu,Daniel Racoceanu,Rui Venâncio +73 more
TL;DR: In the setting of a challenge competition, some deep learning algorithms achieved better diagnostic performance than a panel of 11 pathologists participating in a simulation exercise designed to mimic routine pathology workflow; algorithm performance was comparable with an expert pathologist interpreting whole-slide images without time constraints.
Journal ArticleDOI
From Detection of Individual Metastases to Classification of Lymph Node Status at the Patient Level: The CAMELYON17 Challenge
Péter Bándi,Oscar Geessink,Quirine F. Manson,Marcory C. R. F. van Dijk,Maschenka Balkenhol,Meyke Hermsen,Babak Ehteshami Bejnordi,Byungjae Lee,Kyunghyun Paeng,Aoxiao Zhong,Quanzheng Li,Farhad Ghazvinian Zanjani,Svitlana Zinger,Keisuke Fukuta,Daisuke Komura,Vlado Ovtcharov,Shenghua Cheng,Shaoqun Zeng,Jeppe Thagaard,Anders Bjorholm Dahl,Huangjing Lin,Hao Chen,Ludwig Jacobsson,Martin Hedlund,Melih cetin,Eren Halici,Hunter Jackson,Richard J. Chen,Fabian Both,Jörg Franke,Heidi V.N. Küsters-Vandevelde,Willem Vreuls,Peter Bult,Bram van Ginneken,Jeroen van der Laak,Geert Litjens +35 more
TL;DR: It is shown that simple combinations of the top algorithms result in higher kappa metric values than any algorithm individually, with 0.93 for the best combination.
Journal ArticleDOI
1399 H&E-stained sentinel lymph node sections of breast cancer patients: the CAMELYON dataset.
Geert Litjens,Péter Bándi,Babak Ehteshami Bejnordi,Oscar Geessink,Maschenka Balkenhol,Peter Bult,Altuna Halilovic,Meyke Hermsen,Rob van de Loo,Rob Vogels,Quirine F. Manson,Nikolas Stathonikos,Alexi Baidoshvili,Paul J. van Diest,Carla Wauters,Marcory C. R. F. van Dijk,Jeroen van der Laak +16 more
TL;DR: A unique dataset of annotated, whole-slide digital histopathology images has been provided with high potential for re-use, in 3 terabytes of data in the context of the CAMELYON16 and CAMELYon17 Grand Challenges.
Proceedings ArticleDOI
The importance of stain normalization in colorectal tissue classification with convolutional networks
Francesco Ciompi,Oscar Geessink,Babak Ehteshami Bejnordi,Gabriel Silva de Souza,Alexi Baidoshvili,Geert Litjens,Bram van Ginneken,Iris D. Nagtegaal,Jeroen van der Laak +8 more
TL;DR: This paper proposes a system for CRC tissue classification based on convolutional networks (ConvNets), investigates the importance of stain normalization in tissue classification of CRC tissue samples in H&E-stained images, and reports the performance of ConvNets on a cohort of rectal cancer samples and on an independent publicly available dataset of colorectal H &E images.
Journal ArticleDOI
Learning to detect lymphocytes in immunohistochemistry with deep learning
Zaneta Swiderska-Chadaj,Hans Pinckaers,Mart van Rijthoven,Maschenka Balkenhol,Margarita Melnikova,Margarita Melnikova,Oscar Geessink,Quirine F. Manson,Mark E. Sherman,António Polónia,Jeremy Parry,Mustapha Abubakar,Geert Litjens,Jeroen van der Laak,Jeroen van der Laak,Francesco Ciompi +15 more
TL;DR: A dataset of 171,166 manually annotated CD3+ and CD8+ cells is built, which is used to train deep learning algorithms for automatic detection of lymphocytes in histopathology images to better quantify immune response and investigates the effectiveness of four deep learning based methods.