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Qi Dou
Researcher at The Chinese University of Hong Kong
Publications - 203
Citations - 17127
Qi Dou is an academic researcher from The Chinese University of Hong Kong. The author has contributed to research in topics: Computer science & Segmentation. The author has an hindex of 48, co-authored 162 publications receiving 10280 citations. Previous affiliations of Qi Dou include Imperial College London & Carnegie Mellon University.
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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.
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H-DenseUNet: Hybrid Densely Connected UNet for Liver and Tumor Segmentation From CT Volumes
TL;DR: This work proposes a novel hybrid densely connected UNet (H-DenseUNet), which consists of a 2-D Dense UNet for efficiently extracting intra-slice features and a 3-D counterpart for hierarchically aggregating volumetric contexts under the spirit of the auto-context algorithm for liver and tumor segmentation.
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Automated Melanoma Recognition in Dermoscopy Images via Very Deep Residual Networks
TL;DR: This study corroborates that very deep CNNs with effective training mechanisms can be employed to solve complicated medical image analysis tasks, even with limited training data.
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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.
Journal ArticleDOI
VoxResNet: Deep voxelwise residual networks for brain segmentation from 3D MR images
TL;DR: An auto‐context version of the VoxResNet is proposed by combining the low‐level image appearance features, implicit shape information, and high‐level context together for further improving the segmentation performance, and achieved the best performance in the 2013 MICCAI MRBrainS challenge.