Recent advances in convolutional neural networks
Jiuxiang Gu,Zhenhua Wang,Jason Kuen,Lianyang Ma,Amir Shahroudy,Bing Shuai,Ting Liu,Xingxing Wang,Gang Wang,Jianfei Cai,Tsuhan Chen +10 more
TLDR
A broad survey of the recent advances in convolutional neural networks can be found in this article, where the authors discuss the improvements of CNN on different aspects, namely, layer design, activation function, loss function, regularization, optimization and fast computation.About:
This article is published in Pattern Recognition.The article was published on 2018-05-01 and is currently open access. It has received 3125 citations till now. The article focuses on the topics: Deep learning & Convolutional neural network.read more
Citations
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Journal ArticleDOI
Deep Convolutional Encoder-Decoder Networks for Uncertainty Quantification of Dynamic Multiphase Flow in Heterogeneous Media
TL;DR: In this paper, a deep convolutional encoder-decoder neural network was proposed to characterize the high-dimensional time-dependent outputs of the dynamic multi-phase flow model with a 2500-dimensional stochastic permeability field.
Journal ArticleDOI
Convolutional neural networks for dental image diagnostics: A scoping review.
TL;DR: A scoping review of studies applying CNN on dental image material found most studies found the CNN to perform similar to dentists, thereby assisting dentists in a more comprehensive, systematic and faster evaluation and documentation of dental images.
Proceedings ArticleDOI
Deeply-supervised CNN for prostate segmentation
TL;DR: The proposed model can effectively detect the prostate region with additional deeply supervised layers compared with other approaches and significant segmentation accuracy improvement has been achieved by the method compared to other reported approaches.
Journal ArticleDOI
Theory building with big data-driven research – Moving away from the “What” towards the “Why”
TL;DR: Insight is provided on the methodological adaptations required in “big data studies” to be converted into “IS research” and contribute to theory building in information systems.
Journal ArticleDOI
IDRiD: Diabetic Retinopathy – Segmentation and Grading Challenge
Prasanna Porwal,Prasanna Porwal,Samiksha Pachade,Manesh Kokare,Girish Deshmukh,Jaemin Son,Woong Bae,Lihong Liu,Jianzong Wang,Xinhui Liu,Liangxin Gao,Tian Bo Wu,Jing Xiao,Fengyan Wang,Baocai Yin,Yunzhi Wang,Gopichandh Danala,Linsheng He,Yoon-Ho Choi,Yeong Chan Lee,Sang Hyuk Jung,Zhongyu Li,Xiaodan Sui,Junyan Wu,Xiaolong Li,Ting Zhou,Janos Toth,Agnes Baran,Avinash Kori,Sai Saketh Chennamsetty,Mohammed Safwan,Varghese Alex,Xingzheng Lyu,Li Cheng,Qinhao Chu,Pengcheng Li,Xin Ji,Sanyuan Zhang,Shen Yaxin,Ling Dai,Oindrila Saha,Rachana Sathish,Tânia Melo,Teresa Araújo,Balazs Harangi,Bin Sheng,Ruogu Fang,Debdoot Sheet,Andras Hajdu,Yuanjie Zheng,Ana Maria Mendonça,Shaoting Zhang,Aurélio Campilho,Bin Zheng,Dinggang Shen,Luca Giancardo,Gwenole Quellec,Fabrice Meriaudeau +57 more
TL;DR: The set-up and results of this challenge that is primarily based on Indian Diabetic Retinopathy Image Dataset (IDRiD), which received a positive response from the scientific community, have the potential to enable new developments in retinal image analysis and image-based DR screening in particular.
References
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Gradient-based learning applied to document recognition
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