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Deep learning

How much off-the-shelf knowledge is transferable from natural images to pathology images?

Xingyu Li, Konstantinos N. Plataniotis

The data dimensionality reduction and bad data detection in the process of smart grid reconstruction through machine learning

Bo Yu, Zheng Wang, [ ... ], Ruixin Gou

Deep learning: To better understand how human activities affect the value of ecosystem services—A case study of Nanjing

Chang Liu, Yi Qi, [ ... ], Tianhua Ni

Risk factor analysis combined with deep learning in the risk assessment of overseas investment of enterprises

Xiuyan Xu

Semantic segmentation of HeLa cells: An objective comparison between one traditional algorithm and four deep-learning architectures

Cefa Karabağ, Martin L. Jones, [ ... ], Constantino Carlos Reyes-Aldasoro

On the performance of fusion based planet-scope and Sentinel-2 data for crop classification using inception inspired deep convolutional neural network

Nasru Minallah, Mohsin Tariq, [ ... ], Samir Brahim Belhaouari

Sociodemographic data and APOE-ε4 augmentation for MRI-based detection of amnestic mild cognitive impairment using deep learning systems

Obioma Pelka, Christoph M. Friedrich, [ ... ], for the Alzheimer’s Disease Neuroimaging Initiative

Deep neural network models for identifying incident dementia using claims and EHR datasets

Vijay S. Nori, Christopher A. Hane, [ ... ], Paul A. Bleicher

Automatic image annotation method based on a convolutional neural network with threshold optimization

Jianfang Cao, Aidi Zhao, Zibang Zhang

Joint disc and cup segmentation based on recurrent fully convolutional network

Jing Gao, Yun Jiang, Hai Zhang, Falin Wang

Group-based local adaptive deep multiple kernel learning with lp norm

Shengbing Ren, Fa Liu, [ ... ], Chaudry Naeem Siddique

Inconsistency in the use of the term “validation” in studies reporting the performance of deep learning algorithms in providing diagnosis from medical imaging

Dong Wook Kim, Hye Young Jang, [ ... ], Seong Ho Park

Study of low-dose PET image recovery using supervised learning with CycleGAN

Kui Zhao, Long Zhou, [ ... ], Hongwei Ye

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