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Transfer learning using a multi-scale and multi-network ensemble for skin lesion classification
Transfer learning using a multi-scale and multi-network ensemble for skin lesion classification
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Transfer learning using a multi-scale and multi-network ensemble for skin lesion classification

Mahbod, Amirreza ; Schaefer, Gerald ; Wang, Chunliang ; Dorffner, Georg ; Ecker, Rupert ; Ellinger, Isabella

Computer methods and programs in biomedicine, 2020-09, Vol.193, p.105475-105475, Article 105475 [Periódico revisado por pares]

Ireland: Elsevier B.V

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Deep convolutional neural network for the automated detection and diagnosis of seizure using EEG signals
Deep convolutional neural network for the automated detection and diagnosis of seizure using EEG signals
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Deep convolutional neural network for the automated detection and diagnosis of seizure using EEG signals

Acharya, U. Rajendra ; Oh, Shu Lih ; Hagiwara, Yuki ; Tan, Jen Hong ; Adeli, Hojjat

Computers in biology and medicine, 2018-09, Vol.100, p.270-278 [Periódico revisado por pares]

United States: Elsevier Ltd

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Improving Computer-Aided Detection Using Convolutional Neural Networks and Random View Aggregation
Improving Computer-Aided Detection Using Convolutional Neural Networks and Random View Aggregation
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Improving Computer-Aided Detection Using Convolutional Neural Networks and Random View Aggregation

Roth, Holger R. ; Le Lu ; Jiamin Liu ; Jianhua Yao ; Seff, Ari ; Cherry, Kevin ; Kim, Lauren ; Summers, Ronald M.

IEEE transactions on medical imaging, 2016-05, Vol.35 (5), p.1170-1181

United States: IEEE

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A Weakly-Supervised Framework for COVID-19 Classification and Lesion Localization From Chest CT
A Weakly-Supervised Framework for COVID-19 Classification and Lesion Localization From Chest CT
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A Weakly-Supervised Framework for COVID-19 Classification and Lesion Localization From Chest CT

Wang, Xinggang ; Deng, Xianbo ; Fu, Qing ; Zhou, Qiang ; Feng, Jiapei ; Ma, Hui ; Liu, Wenyu ; Zheng, Chuansheng

IEEE transactions on medical imaging, 2020-08, Vol.39 (8), p.2615-2625

United States: IEEE

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5
Weakly-supervised convolutional neural networks for multimodal image registration
Weakly-supervised convolutional neural networks for multimodal image registration
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Weakly-supervised convolutional neural networks for multimodal image registration

Hu, Yipeng ; Modat, Marc ; Gibson, Eli ; Li, Wenqi ; Ghavami, Nooshin ; Bonmati, Ester ; Wang, Guotai ; Bandula, Steven ; Moore, Caroline M. ; Emberton, Mark ; Ourselin, Sébastien ; Noble, J. Alison ; Barratt, Dean C. ; Vercauteren, Tom

Medical image analysis, 2018-10, Vol.49, p.1-13 [Periódico revisado por pares]

Netherlands: Elsevier B.V

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6
Deep Learning Techniques for Automatic MRI Cardiac Multi-Structures Segmentation and Diagnosis: Is the Problem Solved?
Deep Learning Techniques for Automatic MRI Cardiac Multi-Structures Segmentation and Diagnosis: Is the Problem Solved?
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Deep Learning Techniques for Automatic MRI Cardiac Multi-Structures Segmentation and Diagnosis: Is the Problem Solved?

Bernard, Olivier ; Lalande, Alain ; Zotti, Clement ; Cervenansky, Frederick ; Yang, Xin ; Heng, Pheng-Ann ; Cetin, Irem ; Lekadir, Karim ; Camara, Oscar ; Gonzalez Ballester, Miguel Angel ; Sanroma, Gerard ; Napel, Sandy ; Petersen, Steffen ; Tziritas, Georgios ; Grinias, Elias ; Khened, Mahendra ; Kollerathu, Varghese Alex ; Krishnamurthi, Ganapathy ; Rohe, Marc-Michel ; Pennec, Xavier ; Sermesant, Maxime ; Isensee, Fabian ; Jager, Paul ; Maier-Hein, Klaus H. ; Full, Peter M. ; Wolf, Ivo ; Engelhardt, Sandy ; Baumgartner, Christian F. ; Koch, Lisa M. ; Wolterink, Jelmer M. ; Isgum, Ivana ; Jang, Yeonggul ; Hong, Yoonmi ; Patravali, Jay ; Jain, Shubham ; Humbert, Olivier ; Jodoin, Pierre-Marc

IEEE transactions on medical imaging, 2018-11, Vol.37 (11), p.2514-2525

United States: IEEE

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Application of deep learning technique to manage COVID-19 in routine clinical practice using CT images: Results of 10 convolutional neural networks
Application of deep learning technique to manage COVID-19 in routine clinical practice using CT images: Results of 10 convolutional neural networks
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Application of deep learning technique to manage COVID-19 in routine clinical practice using CT images: Results of 10 convolutional neural networks

Ardakani, Ali Abbasian ; Kanafi, Alireza Rajabzadeh ; Acharya, U. Rajendra ; Khadem, Nazanin ; Mohammadi, Afshin

Computers in biology and medicine, 2020-06, Vol.121, p.103795-103795, Article 103795 [Periódico revisado por pares]

United States: Elsevier Ltd

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8
Ultrasound-based differentiation of malignant and benign thyroid Nodules: An extreme learning machine approach
Ultrasound-based differentiation of malignant and benign thyroid Nodules: An extreme learning machine approach
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Ultrasound-based differentiation of malignant and benign thyroid Nodules: An extreme learning machine approach

Xia, Jianfu ; Chen, Huiling ; Li, Qiang ; Zhou, Minda ; Chen, Limin ; Cai, Zhennao ; Fang, Yang ; Zhou, Hong

Computer methods and programs in biomedicine, 2017-08, Vol.147, p.37-49 [Periódico revisado por pares]

Ireland: Elsevier B.V

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9
Physician stress and burnout: the impact of health information technology
Physician stress and burnout: the impact of health information technology
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Physician stress and burnout: the impact of health information technology

Gardner, Rebekah L ; Cooper, Emily ; Haskell, Jacqueline ; Harris, Daniel A ; Poplau, Sara ; Kroth, Philip J ; Linzer, Mark

Journal of the American Medical Informatics Association : JAMIA, 2019-02, Vol.26 (2), p.106-114 [Periódico revisado por pares]

England: Oxford University Press

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10
Telemedicine and healthcare disparities: a cohort study in a large healthcare system in New York City during COVID-19
Telemedicine and healthcare disparities: a cohort study in a large healthcare system in New York City during COVID-19
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Telemedicine and healthcare disparities: a cohort study in a large healthcare system in New York City during COVID-19

Chunara, Rumi ; Zhao, Yuan ; Chen, Ji ; Lawrence, Katharine ; Testa, Paul A ; Nov, Oded ; Mann, Devin M

Journal of the American Medical Informatics Association : JAMIA, 2021-01, Vol.28 (1), p.33-41 [Periódico revisado por pares]

England: Oxford University Press

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