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1. Full Text Brain tumor segmentation with Deep Neural Networks
by Havaei, Mohammad and Davy, Axel and Warde-Farley, David and Biard, Antoine and Courville, Aaron and Bengio, Yoshua and Pal, Chris and Jodoin, Pierre-Marc and Larochelle, Hugo
Medical image analysis, ISSN 1361-8415, 01/2017, Volume 35, pp. 18 - 31
Brain tumor segmentation | Deep neural networks | Convolutional neural networks | Cascaded convolutional neural networks | Engineering | Computer Science, Interdisciplinary Applications | Life Sciences & Biomedicine | Technology | Engineering, Biomedical | Radiology, Nuclear Medicine & Medical Imaging | Computer Science | Computer Science, Artificial Intelligence | Science & Technology | Glioblastoma - diagnostic imaging | Brain Neoplasms - diagnostic imaging | Humans | Magnetic Resonance Imaging - methods | Image Processing, Computer-Assisted - methods | Machine Learning | Neural Networks (Computer) | Neural networks | Analysis | Brain tumors | Machine learning | Brain | Computer vision | Segmentation | Image processing | Artificial neural networks | Image segmentation | Learning algorithms | Magnetic resonance imaging | Tumors | Brain diseases | Index Medicus
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2. Full Text Recent advances in convolutional neural networks
by Gu, Jiuxiang and Wang, Zhenhua and Kuen, Jason and Ma, Lianyang and Shahroudy, Amir and Shuai, Bing and Liu, Ting and Wang, Xingxing and Wang, Gang and Cai, Jianfei and Chen, Tsuhan
Pattern recognition, ISSN 0031-3203, 05/2018, Volume 77, pp. 354 - 377
Deep learning | Convolutional neural network | Engineering, Electrical & Electronic | Engineering | Computer Science, Artificial Intelligence | Technology | Computer Science | Science & Technology | Computer science | Language processing | Computational linguistics | Machine vision | Neural networks | Natural language interfaces
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3. Full Text Pansharpening by convolutional neural networks
by Masi, Giuseppe and Cozzolino, Davide and Verdoliva, Luisa and Scarpa, Giuseppe
Remote sensing (Basel, Switzerland), ISSN 2072-4292, 07/2016, Volume 8, Issue 7, p. 594
Super-resolution | Segmentation | Convolutional neural networks | Multiresolution | Enhancement | Machine learning | Technology | Remote Sensing | Science & Technology | multiresolution | segmentation | enhancement | convolutional neural networks | machine learning | super-resolution
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4. Full Text Convolutional Neural Networks for Large-Scale Remote-Sensing Image Classification
by Maggiori, Emmanuel and Tarabalka, Yuliya and Charpiat, Guillaume and Alliez, Pierre
IEEE transactions on geoscience and remote sensing, ISSN 0196-2892, 02/2017, Volume 55, Issue 2, pp. 645 - 657
Context | Training | convolutional neural networks (CNNs) | deep learning | Satellites | Neurons | Training data | Classification | Biological neural networks | Remote sensing | satellite images | Engineering | Physical Sciences | Technology | Engineering, Electrical & Electronic | Remote Sensing | Imaging Science & Photographic Technology | Science & Technology | Geochemistry & Geophysics | Neural networks | Research | Artificial neural networks | Satellite sensing | Data | Imagery | Satellite imagery | Maps | Localization | Framework | Image classification | Computer Vision and Pattern Recognition | Image Processing | Computer Science
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5. Full Text Convolutional neural networks for hyperspectral image classification
by Yu, Shiqi and Jia, Sen and Xu, Chunyan
Neurocomputing (Amsterdam), ISSN 0925-2312, 01/2017, Volume 219, pp. 88 - 98
Deep learning | Convolutional neural networks | Hyperspectral image classification | Computer Science, Artificial Intelligence | Technology | Computer Science | Science & Technology | Computer science | Neural networks | Analysis
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6. Full Text Weakly-supervised convolutional neural networks for multimodal image registration
by Hu, Yipeng and Modat, Marc and Gibson, Eli and Li, Wenqi and Ghavami, Nooshin and Bonmati, Ester and Wang, Guotai and Bandula, Steven and Moore, Caroline M and Emberton, Mark and Ourselin, Sébastien and Noble, J. Alison and Barratt, Dean C and Vercauteren, Tom
Medical image analysis, ISSN 1361-8415, 10/2018, Volume 49, pp. 1 - 13
Image-guided intervention | Weakly-supervised learning | Prostate cancer | Convolutional neural network | Medical image registration | Engineering | Computer Science, Interdisciplinary Applications | Life Sciences & Biomedicine | Technology | Engineering, Biomedical | Radiology, Nuclear Medicine & Medical Imaging | Computer Science | Computer Science, Artificial Intelligence | Science & Technology | Magnetic Resonance Imaging | Algorithms | Prostate - anatomy & histology | Image Processing, Computer-Assisted | Humans | Prostate - diagnostic imaging | Ultrasonography | Prostatic Neoplasms - diagnostic imaging | Male | Imaging, Three-Dimensional | Anatomic Landmarks | Neural Networks (Computer) | Neural networks | Medical imaging equipment | Biomedical engineering | Image registration | Transformation | Medical imaging | Nuclear magnetic resonance--NMR | Deformation | Ducts | Glands | Magnetic resonance | Artificial neural networks | Organs | Inference | Formability | Labels | Centroids | 3-D printers | Patients | Training | Magnetic resonance imaging | Landmarks | Prostate | Ultrasound | Index Medicus
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7. Full Text KIKI‐net: cross‐domain convolutional neural networks for reconstructing undersampled magnetic resonance images
by Eo, Taejoon and Jun, Yohan and Kim, Taeseong and Jang, Jinseong and Lee, Ho‐Joon and Hwang, Dosik
Magnetic resonance in medicine, ISSN 0740-3194, 11/2018, Volume 80, Issue 5, pp. 2188 - 2201
cross‐domain deep learning | convolutional neural networks | image reconstruction | MRI acceleration | k‐space completion | cross-domain deep learning | k-space completion | Life Sciences & Biomedicine | Radiology, Nuclear Medicine & Medical Imaging | Science & Technology | Neural networks | Performance evaluation | Neuroimaging | Fourier transforms | Medical imaging | Image processing | Neurodegenerative diseases | Data acquisition | Artificial neural networks | Consistency | Image reconstruction | Artifacts | Neurology | Algorithms | Magnetic resonance imaging | Restoration | Aliasing | Data recovery | Data sets | Alzheimers disease | Alzheimer's disease
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