B
Bram van Ginneken
Researcher at Radboud University Nijmegen
Publications - 441
Citations - 42580
Bram van Ginneken is an academic researcher from Radboud University Nijmegen. The author has contributed to research in topics: Segmentation & Image segmentation. The author has an hindex of 79, co-authored 412 publications receiving 31252 citations. Previous affiliations of Bram van Ginneken include University of Groningen & Utrecht University.
Papers
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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.
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Guest Editorial Deep Learning in Medical Imaging: Overview and Future Promise of an Exciting New Technique
TL;DR: The papers in this special section focus on the technology and applications supported by deep learning, which have proven to be powerful tools for a broad range of computer vision tasks.
Journal ArticleDOI
Reflectance and texture of real-world surfaces
TL;DR: A new texture representation called the BTF (bidirectional texture function) which captures the variation in texture with illumination and viewing direction is discussed, and a BTF database with image textures from over 60 different samples, each observed with over 200 different combinations of viewing and illumination directions is presented.
Journal ArticleDOI
Pulmonary Nodule Detection in CT Images: False Positive Reduction Using Multi-View Convolutional Networks
Arnaud Arindra Adiyoso Setio,Francesco Ciompi,Geert Litjens,Paul K. Gerke,Colin Jacobs,Sarah J. van Riel,Mathilde M. W. Wille,Matiullah Naqibullah,Clara I. Sánchez,Bram van Ginneken +9 more
TL;DR: It was showed that the proposed multi-view ConvNets is highly suited to be used for false positive reduction of a CAD system.