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Babak Ehteshami Bejnordi
Researcher at Qualcomm
Publications - 43
Citations - 13363
Babak Ehteshami Bejnordi is an academic researcher from Qualcomm. The author has contributed to research in topics: Computer science & Artificial neural network. The author has an hindex of 21, co-authored 39 publications receiving 8901 citations. Previous affiliations of Babak Ehteshami Bejnordi include Radboud University Nijmegen & Sahlgrenska University Hospital.
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Journal ArticleDOI
A survey on deep learning in medical image analysis
Geert Litjens,Thijs Kooi,Babak Ehteshami Bejnordi,Arnaud Arindra Adiyoso Setio,Francesco Ciompi,Mohsen Ghafoorian,Jeroen van der Laak,Bram van Ginneken,Clara I. Sánchez +8 more
TL;DR: This paper reviews the major deep learning concepts pertinent to medical image analysis and summarizes over 300 contributions to the field, most of which appeared in the last year, to survey the use of deep learning for image classification, object detection, segmentation, registration, and other tasks.
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
Stain Specific Standardization of Whole-Slide Histopathological Images
Babak Ehteshami Bejnordi,Geert Litjens,Nadya Timofeeva,Irene Otte-Höller,Andre Homeyer,Nico Karssemeijer,Jeroen van der Laak +6 more
TL;DR: The results of the empirical evaluations collectively demonstrate the potential contribution of the proposed standardization algorithm to improved diagnostic accuracy and consistency in computer-aided diagnosis for histopathology data.
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.