Low-dose x-ray tomography through a deep convolutional neural network
Abstract Synchrotron-based X-ray tomography offers the potential for rapid large-scale reconstructions of the interiors of materials and biological tissue at fine resolution. However, for radiation sensitive samples, there remain fundamental trade-offs between damaging samples during longer acquisit...
Saved in:
Main Authors: | Xiaogang Yang, Vincent De Andrade, William Scullin, Eva L. Dyer, Narayanan Kasthuri, Francesco De Carlo, Doğa Gürsoy |
---|---|
Format: | article |
Language: | EN |
Published: |
Nature Portfolio
2018
|
Subjects: | |
Online Access: | https://doaj.org/article/f2c3eb2baba2489aa424e39aaef3f1eb |
Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
-
Self-Supervised Deep Convolutional Neural Network for Chest X-Ray Classification
by: Matej Gazda, et al.
Published: (2021) -
AngioNet: a convolutional neural network for vessel segmentation in X-ray angiography
by: Kritika Iyer, et al.
Published: (2021) -
A convolutional neural network for defect classification in Bragg coherent X-ray diffraction
by: Bruce Lim, et al.
Published: (2021) -
Noise reduction in X-ray photon correlation spectroscopy with convolutional neural networks encoder–decoder models
by: Tatiana Konstantinova, et al.
Published: (2021) -
An Effective Convolutional Neural Network Model for the Early Detection of COVID-19 Using Chest X-ray Images
by: Muhammad Shoaib Farooq, et al.
Published: (2021)