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Arnaud Arindra Adiyoso Setio
Researcher at Siemens
Publications - 30
Citations - 12338
Arnaud Arindra Adiyoso Setio is an academic researcher from Siemens. The author has contributed to research in topics: Deep learning & Supervised learning. The author has an hindex of 14, co-authored 30 publications receiving 8634 citations. Previous affiliations of Arnaud Arindra Adiyoso Setio include Radboud University Nijmegen & Bandung Institute of Technology.
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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
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.
Journal ArticleDOI
Validation, comparison, and combination of algorithms for automatic detection of pulmonary nodules in computed tomography images: The LUNA16 challenge.
Arnaud Arindra Adiyoso Setio,Alberto Traverso,Thomas de Bel,Moira S.N. Berens,Cas van den Bogaard,Piergiorgio Cerello,Hao Chen,Qi Dou,Maria Evelina Fantacci,Bram Geurts,Robbert van der Gugten,Pheng-Ann Heng,Bart Jansen,Michael M.J. de Kaste,Valentin Kotov,Jack Yu-Hung Lin,Jeroen Manders,Alexander Sóñora-Mengana,Juan C. García-Naranjo,Evgenia Papavasileiou,Mathias Prokop,M. Saletta,Cornelia M. Schaefer-Prokop,Ernst T. Scholten,Luuk Scholten,Miranda M. Snoeren,Ernesto Lopez Torres,Jef Vandemeulebroucke,Nicole Walasek,Guido Zuidhof,Bram van Ginneken,Colin Jacobs +31 more
TL;DR: The LUNA16 challenge is described, an objective evaluation framework for automatic nodule detection algorithms using the largest publicly available reference database of chest CT scans, the LIDC‐IDRI data set, and the results so far are presented.
Proceedings ArticleDOI
Memory-centric accelerator design for Convolutional Neural Networks
TL;DR: It is shown that the effects of the memory bottleneck can be reduced by a flexible memory hierarchy that supports the complex data access patterns in CNN workload and ensures that on-chip memory size is minimized, which reduces area and energy usage.
Proceedings ArticleDOI
Off-the-shelf convolutional neural network features for pulmonary nodule detection in computed tomography scans
TL;DR: This work uses the features from one such network, OverFeat, trained for object detection in natural images, for nodule detection in computed tomography scans and concludes that CNN features have great potential to be used for detection tasks in volumetric medical data.