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Amir Jamaludin

Showing results (1-10 of 12) with videos related to

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Medical Image Analysis|July 31, 2017
SpineNet: Automated classification and evidence visualization in spinal MRIsAmir Jamaludin, Timor Kadir, Andrew Zisserman
Scientific Reports|July 1, 2024
Automated detection, labelling and radiological grading of clinical spinal MRIsRhydian Windsor, Amir Jamaludin, Timor Kadir, et al.
Bone|April 20, 2023
Automated measurement of size of spinal curve in population-based cohorts: Validation of a method based on total body dual energy X-ray absorptiometry scansAmir Jamaludin, Jeremy Fairbank, Ian Harding, et al.
Calcified Tissue International|April 20, 2020
Correction to: Identifying Scoliosis in Population‑Based Cohorts: Automation of a Validated Method Based on Total Body Dual Energy X‑ray Absorptiometry ScansAmir Jamaludin, Jeremy Fairbank, Ian Harding, et al.
Calcified Tissue International|January 11, 2020
Identifying Scoliosis in Population-Based Cohorts: Automation of a Validated Method Based on Total Body Dual Energy X-ray Absorptiometry ScansAmir Jamaludin, Jeremy Fairbank, Ian Harding, et al.
European Spine Journal : Official Publication of the European Spine Society, the European Spinal Deformity Society, and the European Section of the Cervical Spine Research Society|February 8, 2017
ISSLS PRIZE IN BIOENGINEERING SCIENCE 2017: Automation of reading of radiological features from magnetic resonance images (MRIs) of the lumbar spine without human intervention is comparable with an expert radiologistAmir Jamaludin, Meelis Lootus, Timor Kadir, et al.
Skeletal Radiology|July 2, 2020
Accurate prediction of lumbar microdecompression level with an automated MRI grading systemBrandon L Roller, Robert D Boutin, Tadhg J O'Gara, et al.
European Spine Journal : Official Publication of the European Spine Society, the European Spinal Deformity Society, and the European Section of the Cervical Spine Research Society|July 14, 2022
External validation of the deep learning system "SpineNet" for grading radiological features of degeneration on MRIs of the lumbar spineAlexandra Grob, Markus Loibl, Amir Jamaludin, et al.
Spine|February 2, 2023
External Validation of SpineNet, an Open-Source Deep Learning Model for Grading Lumbar Disk Degeneration MRI Features, Using the Northern Finland Birth Cohort 1966Terence P McSweeney, Aleksei Tiulpin, Simo Saarakkala, et al.
Plos One|July 6, 2023
A deep learning approach to private data sharing of medical images using conditional generative adversarial networks (GANs)Hanxi Sun, Jason Plawinski, Sajanth Subramaniam, et al.
Pageof 2

Showing results (1-10 of 12) with videos related to

Sort By:
Pageof 2
Medical Image Analysis|July 31, 2017
SpineNet: Automated classification and evidence visualization in spinal MRIsAmir Jamaludin, Timor Kadir, Andrew Zisserman
Scientific Reports|July 1, 2024
Automated detection, labelling and radiological grading of clinical spinal MRIsRhydian Windsor, Amir Jamaludin, Timor Kadir, et al.
Bone|April 20, 2023
Automated measurement of size of spinal curve in population-based cohorts: Validation of a method based on total body dual energy X-ray absorptiometry scansAmir Jamaludin, Jeremy Fairbank, Ian Harding, et al.
Calcified Tissue International|April 20, 2020
Correction to: Identifying Scoliosis in Population‑Based Cohorts: Automation of a Validated Method Based on Total Body Dual Energy X‑ray Absorptiometry ScansAmir Jamaludin, Jeremy Fairbank, Ian Harding, et al.
Calcified Tissue International|January 11, 2020
Identifying Scoliosis in Population-Based Cohorts: Automation of a Validated Method Based on Total Body Dual Energy X-ray Absorptiometry ScansAmir Jamaludin, Jeremy Fairbank, Ian Harding, et al.
European Spine Journal : Official Publication of the European Spine Society, the European Spinal Deformity Society, and the European Section of the Cervical Spine Research Society|February 8, 2017
ISSLS PRIZE IN BIOENGINEERING SCIENCE 2017: Automation of reading of radiological features from magnetic resonance images (MRIs) of the lumbar spine without human intervention is comparable with an expert radiologistAmir Jamaludin, Meelis Lootus, Timor Kadir, et al.
Skeletal Radiology|July 2, 2020
Accurate prediction of lumbar microdecompression level with an automated MRI grading systemBrandon L Roller, Robert D Boutin, Tadhg J O'Gara, et al.
European Spine Journal : Official Publication of the European Spine Society, the European Spinal Deformity Society, and the European Section of the Cervical Spine Research Society|July 14, 2022
External validation of the deep learning system "SpineNet" for grading radiological features of degeneration on MRIs of the lumbar spineAlexandra Grob, Markus Loibl, Amir Jamaludin, et al.
Spine|February 2, 2023
External Validation of SpineNet, an Open-Source Deep Learning Model for Grading Lumbar Disk Degeneration MRI Features, Using the Northern Finland Birth Cohort 1966Terence P McSweeney, Aleksei Tiulpin, Simo Saarakkala, et al.
Plos One|July 6, 2023
A deep learning approach to private data sharing of medical images using conditional generative adversarial networks (GANs)Hanxi Sun, Jason Plawinski, Sajanth Subramaniam, et al.
Pageof 2