Results 61 to 70 of about 12,340,531 (359)

Medical imaging deep learning with differential privacy

Scientific Reports, 2021
The successful training of deep learning models for diagnostic deployment in medical imaging applications requires large volumes of data. Such data cannot be procured without consideration for patient privacy, mandated both by legal regulations and ...
Alexander Ziller, Dmitrii Usynin, R. Braren, M. Makowski, D. Rueckert, Georgios Kaissis   +5 more
semanticscholar   +1 more source

Neuroconductor: an R platform for medical imaging analysis [PDF]

, 2019
Neuroconductor (https://neuroconductor.org) is an open-source platform for rapid testing and dissemination of reproducible computational imaging software.
Avants, B., Avants, B., Caffo, B.S., Caffo, B.S., Clayden, J.D., Clayden, J.D., Crainiceanu, C.M., Crainiceanu, C.M., Fortin, J.P., Fortin, J.P., Gherman, A., Gherman, A., Muschelli, J., Muschelli, J., Whitcher, B., Whitcher, B.   +15 more
core   +1 more source

Novel Transfer Learning Approach for Medical Imaging with Limited Labeled Data

Cancers, 2021
Deep learning requires a large amount of data to perform well. However, the field of medical image analysis suffers from a lack of sufficient data for training deep learning models.
Laith Alzubaidi, Muthana Al-Amidie, Ahmed Al-Asadi, A. Humaidi, O. Al-Shamma, M. Fadhel, Jinglan Zhang, J. Santamaría, Y. Duan, Keyvan Farahani, Bardia Yousefi   +10 more
semanticscholar   +1 more source

Theranostic nanoplatform to target macrophages enables the inhibition of atherosclerosis progression and fluorescence imaging of plaque in ApoE(−/−) mice

Journal of Nanobiotechnology, 2021
Background Rupture of atherosclerotic plaque can cause acute malignant heart and cerebrovascular events, such as acute coronary heart disease, stroke and so on, which seriously threaten the safety of human life and property.
Qi Wang, Yong Wang, Siwen Liu, Xuan Sha, Xiaoxi Song, Yue Dai, Mingming Zhao, Lulu Cai, Kai Xu, Jingjing Li   +9 more
doaj   +1 more source

Preparing Medical Imaging Data for Machine Learning.

Radiology, 2020
Artificial intelligence (AI) continues to garner substantial interest in medical imaging. The potential applications are vast and include the entirety of the medical imaging life cycle from image creation to diagnosis to outcome prediction.
M. Willemink, W. A. Koszek, Cailin Hardell, Jie Wu, D. Fleischmann, H. Harvey, L. Folio, R. Summers, D. Rubin, M. Lungren   +9 more
semanticscholar   +1 more source

Novel muon imaging techniques [PDF]

, 2018
Owing to the high penetrating power of high-energy cosmic ray muons, muon imaging techniques can be used to image large bulky objects, especially objects with heavy shielding.
Al Jebali, Ramsey, Clarkson, Tony, Gardner, Simon, Ireland, David, Kaiser, Ralf, Mahon, David, Ryan, Matthew, Shearer, Craig, Yang, Guangliang   +8 more
core   +1 more source

Development of medical imaging sensors

International Journal of Distributed Sensor Networks, 2020
This review describes sensor technologies for medical imaging. Medical imaging plays an extremely important role in medical imaging, and sensor technology affects the quality of imaging results.
Kai-di Feng, Zhenzhen Wang, Yan Yang
doaj   +1 more source

Medical Imaging Workshop for Seniors

MedEdPORTAL, 2015
Introduction This health education workshop is designed to provide an older adult audience with basic information on imaging modalities commonly ordered in an emergency department setting.
James Hogan
doaj   +1 more source

Impact of additional mattresses in emergency CT on the automated patient centering proposed by a 3D camera: a phantom study

Scientific Reports, 2021
To assess the impact of the use of additional mattresses of different thicknesses on radiation dose and image noise based on the patient centering proposed by a 3D camera for CT.
Joël Greffier, Julien Frandon, Hélène de Forges, Aymeric Hamard, Asmaa Belaouni, Jean Baptiste Wahl, Djamel Dabli, Jean Paul Beregi   +7 more
doaj   +1 more source

Gender imbalance in medical imaging datasets produces biased classifiers for computer-aided diagnosis

Proceedings of the National Academy of Sciences of the United States of America, 2020
Artificial intelligence (AI) systems for computer-aided diagnosis and image-based screening are being adopted worldwide by medical institutions. In such a context, generating fair and unbiased classifiers becomes of paramount importance.
Agostina J. Larrazabal, Nicolás Nieto, Victoria Peterson, Diego H. Milone, Enzo Ferrante   +4 more
semanticscholar   +1 more source