Automated Pathogenesis-Based Diagnosis of Lumbar Neural Foraminal Stenosis via Deep Multiscale Multitask Learning
Published 2018 View Full Article
- Home
- Publications
- Publication Search
- Publication Details
Title
Automated Pathogenesis-Based Diagnosis of Lumbar Neural Foraminal Stenosis via Deep Multiscale Multitask Learning
Authors
Keywords
Neural foraminal stenosis, Multiscale learning, Multitask learning, Deep learning
Journal
NEUROINFORMATICS
Volume -, Issue -, Pages -
Publisher
Springer Nature
Online
2018-02-15
DOI
10.1007/s12021-018-9365-1
References
Ask authors/readers for more resources
Related references
Note: Only part of the references are listed.- Effective Uyghur Language Text Detection in Complex Background Images for Traffic Prompt Identification
- (2018) Chenggang Yan et al. IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS
- Supervised Hash Coding With Deep Neural Network for Environment Perception of Intelligent Vehicles
- (2018) Chenggang Yan et al. IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS
- Unsupervised boundary delineation of spinal neural foramina using a multi-feature and adaptive spectral segmentation
- (2017) Xiaoxu He et al. MEDICAL IMAGE ANALYSIS
- Automated segmentation and area estimation of neural foramina with boundary regression model
- (2017) Xiaoxu He et al. PATTERN RECOGNITION
- A multi-center milestone study of clinical vertebral CT segmentation
- (2016) Jianhua Yao et al. COMPUTERIZED MEDICAL IMAGING AND GRAPHICS
- Multi-Modality Vertebra Recognition in Arbitrary Views Using 3D Deformable Hierarchical Model
- (2015) Yunliang Cai et al. IEEE TRANSACTIONS ON MEDICAL IMAGING
- Regression Segmentation for $M^{3}$ Spinal Images
- (2015) Zhijie Wang et al. IEEE TRANSACTIONS ON MEDICAL IMAGING
- Automatic Segmentation of Spinal Canals in CT Images via Iterative Topology Refinement
- (2015) Qian Wang et al. IEEE TRANSACTIONS ON MEDICAL IMAGING
- A New MRI Grading System for Cervical Foraminal Stenosis Based on Axial T2-Weighted Images
- (2015) Sujin Kim et al. KOREAN JOURNAL OF RADIOLOGY
- A Highly Parallel Framework for HEVC Coding Unit Partitioning Tree Decision on Many-core Processors
- (2014) Chenggang Yan et al. IEEE SIGNAL PROCESSING LETTERS
- Efficient Parallel Framework for HEVC Motion Estimation on Many-Core Processors
- (2014) Chenggang Yan et al. IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS FOR VIDEO TECHNOLOGY
- Clinical Correlation of a New MR Imaging Method for Assessing Lumbar Foraminal Stenosis
- (2012) H.-J. Park et al. AMERICAN JOURNAL OF NEURORADIOLOGY
- Morphometric analysis of the lumbar intervertebral foramen in patients with degenerative lumbar scoliosis by multidetector-row computed tomography
- (2012) Yasuhito Kaneko et al. EUROPEAN SPINE JOURNAL
- Intervertebral disc segmentation in MR images using anisotropic oriented flux
- (2012) Max W.K. Law et al. MEDICAL IMAGE ANALYSIS
- Spine detection in CT and MR using iterated marginal space learning
- (2012) B. Michael Kelm et al. MEDICAL IMAGE ANALYSIS
- Spinal Fusion in the United States
- (2011) Sean S. Rajaee et al. SPINE
- A Practical MRI Grading System for Lumbar Foraminal Stenosis
- (2010) Seunghun Lee et al. AMERICAN JOURNAL OF ROENTGENOLOGY
- Labeling of Lumbar Discs Using Both Pixel- and Object-Level Features With a Two-Level Probabilistic Model
- (2010) R S Alomari et al. IEEE TRANSACTIONS ON MEDICAL IMAGING
- Toward a clinical lumbar CAD: herniation diagnosis
- (2010) Raja’ S. Alomari et al. International Journal of Computer Assisted Radiology and Surgery
- Learning-Based Vertebra Detection and Iterative Normalized-Cut Segmentation for Spinal MRI
- (2009) Szu-Hao Huang et al. IEEE TRANSACTIONS ON MEDICAL IMAGING
- The Pascal Visual Object Classes (VOC) Challenge
- (2009) Mark Everingham et al. INTERNATIONAL JOURNAL OF COMPUTER VISION
- Automated detection of spinal centrelines, vertebral bodies and intervertebral discs in CT and MR images of lumbar spine
- (2009) Darko Štern et al. PHYSICS IN MEDICINE AND BIOLOGY
Create your own webinar
Interested in hosting your own webinar? Check the schedule and propose your idea to the Peeref Content Team.
Create NowAsk a Question. Answer a Question.
Quickly pose questions to the entire community. Debate answers and get clarity on the most important issues facing researchers.
Get Started