Zobrazeno 1 - 10
of 22
pro vyhledávání: '"Chenjie Ge"'
Publikováno v:
BMC Medical Imaging, Vol 20, Iss 1, Pp 1-11 (2020)
Abstract Background This paper addresses issues of brain tumor, glioma, classification from four modalities of Magnetic Resonance Image (MRI) scans (i.e., T1 weighted MRI, T1 weighted MRI with contrast-enhanced, T2 weighted MRI and FLAIR). Currently,
Externí odkaz:
https://doaj.org/article/84369f4028ec476fa5e54dc9190f78a3
Publikováno v:
IEEE Access, Vol 8, Pp 22560-22570 (2020)
This paper addresses issues of brain tumor subtype classification using Magnetic Resonance Images (MRIs) from different scanner modalities like T1 weighted, T1 weighted with contrast-enhanced, T2 weighted and FLAIR images. Currently most available gl
Externí odkaz:
https://doaj.org/article/a7156a80fc2048799907cd0f5d58f0fd
Publikováno v:
Experimental and Therapeutic Medicine. 25
Publikováno v:
IEEE Transactions on Instrumentation and Measurement. 70:1-10
This article proposes a novel scheme for analyzing power system measurement data. The main question that we seek answers in this study is on “whether one can find some important patterns that are hidden in the large data of power system measurement
Publikováno v:
BMC Medical Imaging, Vol 20, Iss 1, Pp 1-11 (2020)
BMC Medical Imaging
BMC Medical Imaging
Background This paper addresses issues of brain tumor, glioma, classification from four modalities of Magnetic Resonance Image (MRI) scans (i.e., T1 weighted MRI, T1 weighted MRI with contrast-enhanced, T2 weighted MRI and FLAIR). Currently, many ava
Publikováno v:
IEEE Access, Vol 8, Pp 22560-22570 (2020)
This paper addresses issues of brain tumor subtype classification using Magnetic Resonance Images (MRIs) from different scanner modalities like T1 weighted, T1 weighted with contrast-enhanced, T2 weighted and FLAIR images. Currently most available gl
Autor:
Eddie, de Dios, Muhaddisa Barat, Ali, Irene Yu-Hua, Gu, Tomás Gomez, Vecchio, Chenjie, Ge, Asgeir S, Jakola
Publikováno v:
Acta neurochirurgica. Supplement. 134
The use of deep learning (DL) is rapidly increasing in clinical neuroscience. The term denotes models with multiple sequential layers of learning algorithms, architecturally similar to neural networks of the brain. We provide examples of DL in analyz
Autor:
Asgeir Store Jakola, Muhaddisa Barat Ali, Irene Yu-Hua Gu, Eddie de Dios, Tomás Gómez Vecchio, Chenjie Ge
Publikováno v:
Acta Neurochirurgica Supplement ISBN: 9783030852917
The use of deep learning (DL) is rapidly increasing in clinical neuroscience. The term denotes models with multiple sequential layers of learning algorithms, architecturally similar to neural networks of the brain. We provide examples of DL in analyz
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::081a0c1f67211afa8c0f2222353526ed
https://doi.org/10.1007/978-3-030-85292-4_11
https://doi.org/10.1007/978-3-030-85292-4_11
Publikováno v:
Neurocomputing. 350:60-69
This paper addresses the issue of Alzheimer’s disease (AD) detection from Magnetic Resonance Images (MRIs). Existing AD detection methods rely on global feature learning from the whole brain scans, while depending on the tissue types, AD related fe
Publikováno v:
Electric Power Systems Research. 194:107042
This paper addresses the issue of automatically seeking and identifying daily, weekly and seasonal patterns in harmonic voltage from measurement data at multiple locations. We propose a novel framework that employs deep autoencoder (DAE) followed by