Deep learning classification of inverted papilloma malignant transformation using 3D convolutional neural networks and magnetic resonance imaging

Neural Networks ◽

Deep Learning ◽

Magnetic Resonance ◽

Malignant Transformation ◽

Inverted Papilloma ◽

Editorial on “Convolutional Neural Networks for Automated Classification of Prostate Multiparametric Magnetic Resonance Imaging Based on Image Quality”

Journal of Magnetic Resonance Imaging ◽

10.1002/jmri.27913 ◽

2021 ◽

Author(s):

Valdair F. Muglia ◽

Antonio Carlos Westphalen

Keyword(s):

Neural Networks ◽

Magnetic Resonance ◽

Image Quality ◽

Multiparametric Magnetic Resonance Imaging

Automated Classification ◽

Resonance Imaging ◽

Deep learning approaches using 2D and 3D convolutional neural networks for generating male pelvic synthetic computed tomography from magnetic resonance imaging

Medical Physics ◽

10.1002/mp.13672 ◽

2019 ◽

Vol 46 (9) ◽

pp. 3788-3798 ◽

Cited By ~ 14

Author(s):

Jie Fu ◽

Yingli Yang ◽

Kamal Singhrao ◽

Dan Ruan ◽

Fang‐I Chu ◽

...

Keyword(s):

Computed Tomography ◽

Neural Networks ◽

Deep Learning ◽

Magnetic Resonance ◽

Learning Approaches ◽

Resonance Imaging ◽

2D And 3D

Classification of Brain Tumor from Magnetic Resonance Imaging using Convolutional Neural Networks

International Journal of Advanced Science and Technology ◽

10.33832/ijast.2019.126.04 ◽

2019 ◽

Vol 126 ◽

pp. 31-38

Author(s):

Mohammed-Amine Zyad ◽

Mohamed Gouskir ◽

Belaid Bouikhalene

Keyword(s):

Neural Networks ◽

Brain Tumor ◽

Magnetic Resonance ◽

Deep learning in nasopharyngeal carcinoma: A retrospective cohort study of 3D convolutional neural networks on magnetic resonance imaging

Annals of Oncology ◽

10.1093/annonc/mdz252.057 ◽

2019 ◽

Vol 30 ◽

pp. v471

Author(s):

M.Y. Qiang ◽

X. Lv ◽

C.F. Li ◽

K.Y. Liu ◽

X. Chen ◽

...

Keyword(s):

Neural Networks ◽

Cohort Study ◽

Deep Learning ◽

Magnetic Resonance ◽

Nasopharyngeal Carcinoma ◽

Retrospective Cohort Study ◽

Retrospective Cohort ◽

Convolutional Neural Networks for Automated Detection and Classification of Bone Tumors in Magnetic Resonance Imaging

10.1109/icairc52191.2021.9545036 ◽

2021 ◽

Author(s):

Vlad Georgeanu ◽

Madalin-Lucian Mamuleanu ◽

Dan Selisteanu

Keyword(s):

Neural Networks ◽

Magnetic Resonance ◽

Bone Tumors ◽

Automated Detection ◽

Convolutional Neural Networks for Automated Classification of Prostate Multiparametric Magnetic Resonance Imaging Based on Image Quality

Journal of Magnetic Resonance Imaging ◽

10.1002/jmri.27879 ◽

2021 ◽

Author(s):

Stefano Cipollari ◽

Valerio Guarrasi ◽

Martina Pecoraro ◽

Marco Bicchetti ◽

Emanuele Messina ◽

...

Keyword(s):

Neural Networks ◽

Magnetic Resonance ◽

Image Quality ◽

Multiparametric Magnetic Resonance Imaging

Automated Classification ◽

Resonance Imaging ◽

Deep learning based-classification of dementia in magnetic resonance imaging scans

2019 International Artificial Intelligence and Data Processing Symposium (IDAP) ◽

10.1109/idap.2019.8875961 ◽

2019 ◽

Cited By ~ 1

Author(s):

Hasan Ucuzal ◽

Ahmet K. Arslan ◽

Cemil Colak

Keyword(s):

Deep Learning ◽

Magnetic Resonance ◽

Early Identification of Alzheimer’s Disease Using an Ensemble of 3D Convolutional Neural Networks and Magnetic Resonance Imaging

Advances in Brain Inspired Cognitive Systems - Lecture Notes in Computer Science ◽

10.1007/978-3-030-00563-4_29 ◽

2018 ◽

pp. 303-311

Author(s):

Yuanyuan Chen ◽

Haozhe Jia ◽

Zhaowei Huang ◽

Yong Xia

Keyword(s):

Alzheimer’S Disease ◽

Neural Networks ◽

Alzheimer's Disease ◽

Magnetic Resonance ◽

Early Identification ◽

Artificial Intelligence and Applied Mathematics in Engineering Problems - Lecture Notes on Data Engineering and Communications Technologies ◽

A Deep Learning Model Based on Convolutional Neural Networks for Classification of Magnetic Resonance Prostate Images

10.1007/978-3-030-36178-5_59 ◽

2020 ◽

pp. 701-708

Author(s):

Fatih Uysal ◽

Fırat Hardalaç ◽

Mustafa Koç

Keyword(s):

Neural Networks ◽

Deep Learning ◽

Magnetic Resonance ◽

Learning Model ◽

Model Based ◽

Deep Learning Model

Efficiency Improvement in a Busy Radiology Practice: Determination of Musculoskeletal Magnetic Resonance Imaging Protocol Using Deep-Learning Convolutional Neural Networks

Journal of Digital Imaging ◽

10.1007/s10278-018-0066-y ◽

2018 ◽

Vol 31 (5) ◽

pp. 604-610 ◽

Cited By ~ 23

Author(s):

Young Han Lee

Keyword(s):

Magnetic Resonance Imaging Protocol

Neural Networks ◽

Deep Learning ◽

Magnetic Resonance ◽

Efficiency Improvement ◽

Resonance Imaging ◽

Imaging Protocol ◽

Radiology Practice ◽