Deep learning for high-resolution and high-sensitivity interferometric phase contrast imaging
出版年份 2020 全文链接
标题
Deep learning for high-resolution and high-sensitivity interferometric phase contrast imaging
作者
关键词
-
出版物
Scientific Reports
Volume 10, Issue 1, Pages -
出版商
Springer Science and Business Media LLC
发表日期
2020-06-18
DOI
10.1038/s41598-020-66690-7
参考文献
相关参考文献
注意:仅列出部分参考文献,下载原文获取全部文献信息。- Characterization of the phase sensitivity, visibility, and resolution in a symmetric neutron grating interferometer
- (2019) Youngju Kim et al. REVIEW OF SCIENTIFIC INSTRUMENTS
- A convolutional neural network for sleep stage scoring from raw single-channel EEG
- (2018) Arnaud Sors et al. Biomedical Signal Processing and Control
- Multimodal MR Synthesis via Modality-Invariant Latent Representation
- (2018) Agisilaos Chartsias et al. IEEE TRANSACTIONS ON MEDICAL IMAGING
- Deep convolutional neural network processing of aerial stereo imagery to monitor vulnerable zones near power lines
- (2018) Abdul Qayyum et al. Journal of Applied Remote Sensing
- High-frequency details enhancing DenseNet for super-resolution
- (2018) Fuqiang Zhou et al. NEUROCOMPUTING
- Deep learning based on Batch Normalization for P300 signal detection
- (2018) Mingfei Liu et al. NEUROCOMPUTING
- Single image super-resolution using a deep encoder–decoder symmetrical network with iterative back projection
- (2018) Heng Liu et al. NEUROCOMPUTING
- Using machine-learning to optimize phase contrast in a low-cost cellphone microscope
- (2018) Benedict Diederich et al. PLoS One
- Liver Fibrosis: Deep Convolutional Neural Network for Staging by Using Gadoxetic Acid–enhanced Hepatobiliary Phase MR Images
- (2018) Koichiro Yasaka et al. RADIOLOGY
- Feasibility evaluation of a neutron grating interferometer with an analyzer grating based on a structured scintillator
- (2018) Youngju Kim et al. REVIEW OF SCIENTIFIC INSTRUMENTS
- Ship Detection in Gaofen-3 SAR Images Based on Sea Clutter Distribution Analysis and Deep Convolutional Neural Network
- (2018) Quanzhi An et al. SENSORS
- Automatic Knee Osteoarthritis Diagnosis from Plain Radiographs: A Deep Learning-Based Approach
- (2018) Aleksei Tiulpin et al. Scientific Reports
- Using human brain activity to guide machine learning
- (2018) Ruth C. Fong et al. Scientific Reports
- High-resolution X-ray phase-contrast imaging with a grating interferometer
- (2017) Seung Wook Lee et al. JOURNAL OF THE KOREAN PHYSICAL SOCIETY
- Enhancement of digital radiography image quality using a convolutional neural network
- (2017) Yuewen Sun et al. Journal of X-Ray Science and Technology
- Coupled Deep Autoencoder for Single Image Super-Resolution
- (2017) Kun Zeng et al. IEEE Transactions on Cybernetics
- Image Super-Resolution Using Deep Convolutional Networks
- (2016) Chao Dong et al. IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE
- Fast and flexible X-ray tomography using the ASTRA toolbox
- (2016) Wim van Aarle et al. OPTICS EXPRESS
- A Perspective on Deep Imaging
- (2016) Ge Wang IEEE Access
- Experimental Realisation of High-sensitivity Laboratory X-ray Grating-based Phase-contrast Computed Tomography
- (2016) Lorenz Birnbacher et al. Scientific Reports
- Deep learning as a tool for increased accuracy and efficiency of histopathological diagnosis
- (2016) Geert Litjens et al. Scientific Reports
- The ASTRA Toolbox: A platform for advanced algorithm development in electron tomography
- (2015) Wim van Aarle et al. ULTRAMICROSCOPY
- Guided Image Filtering
- (2012) Kaiming He et al. IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE
- Inverse geometry for grating-based x-ray phase-contrast imaging
- (2009) Tilman Donath et al. JOURNAL OF APPLIED PHYSICS
- Realistic CT simulation using the 4D XCAT phantom
- (2008) W. P. Segars et al. MEDICAL PHYSICS
- Hard-X-ray dark-field imaging using a grating interferometer
- (2008) F. Pfeiffer et al. NATURE MATERIALS
Discover Peeref hubs
Discuss science. Find collaborators. Network.
Join a conversationPublish scientific posters with Peeref
Peeref publishes scientific posters from all research disciplines. Our Diamond Open Access policy means free access to content and no publication fees for authors.
Learn More