Automated Pathogenesis-Based Diagnosis of Lumbar Neural Foraminal Stenosis via Deep Multiscale Multitask Learning

By Zhongyi Han, Benzheng Wei, Stephanie Leung, I. B. Nachum, D. Laidley, Sijie Li. 2018

40 citations recorded by the source corpus.

Subfields: Biomedical Engineering, Pathology and Forensic Medicine, Surgery

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Biomedical Engineering

1. Arvid Lundervold

In Pathology and Forensic Medicine

1. Arvid Lundervold

In Surgery

1. Arvid Lundervold

Citations in the corpus

• An overview of deep learning in medical imaging focusing on MRI (2018, background): “ARTICLE IN PRESS A. Selvikvåg Lundervold, A. Lundervold / Z Med Phys xxx (2018) xxx–xxx 15 Table 2 (Continued) Spine Vertebrae labeling [293] Designed a CNN for detection and labeling of vertebrae in MR images with clinical annotations as training data Intervertebral disc localization [268] 3D multi”
• Deep Learning for Medical Anomaly Detection – A Survey (2020, background): “In a different line of work, a multi-scale multi-task learning framework is proposed in [62] for diagnosis of Lumbar Neural Foraminal Stenosis (LNFS).”