Automated analysis of spinal MRI using deep learning

Automated analysis of spinal MRI using deep learning

<p>The objective of this thesis is the automation of radiological gradings in spinal lumbar Magnetic Resonance Images (MRIs). Solving this is extremely beneficial as this in a way would help in the standardization of gradings especially for back pain research. The output of the research done i...

Szczegółowa specyfikacja

Opis bibliograficzny
1. autor:	Jamaludin, M
Kolejni autorzy:	Kadir, T
Format:	Praca dyplomowa
Język:	English
Wydane:	2017

Podobne zapisy

Automated radiological analysis of spinal MRI
od: Lootus, M
Wydane: (2015)

Automated spinal MRI labelling from reports using a large language model
od: Park, RY, i wsp.
Wydane: (2024)

Automated Segmentation of Brain Tumor MRI Images Using Deep Learning
od: Surendran Rajendran, i wsp.
Wydane: (2023-01-01)

A deep learning pipeline for automatized assessment of spinal MRI
od: Irina Balzer, i wsp.
Wydane: (2022-01-01)

Deep learning for the fully automated segmentation of the inner ear on MRI
od: Akshayaa Vaidyanathan, i wsp.
Wydane: (2021-02-01)

Automatic modic changes classification in spinal MRI
od: Jamaludin, A, i wsp.
Wydane: (2016)

Automated Brain Tumor Segmentation and Classification in MRI Using YOLO-Based Deep Learning
od: Maram Fahaad Almufareh, i wsp.
Wydane: (2024-01-01)

Automated volumetry of meningiomas in contrast-enhanced T1-Weighted MRI using deep learning
od: Takamitsu Iwata, i wsp.
Wydane: (2024-04-01)

Deep learning model for automated detection and classification of degenerative cord signal abnormality, spinal canal and neural foraminal stenosis on cervical spine MRI
od: J.H.J. Tan, i wsp.
Wydane: (2024-01-01)

Spinal disease diagnosis assistant based on MRI images using deep transfer learning methods
od: Junbo Xuan, i wsp.
Wydane: (2023-02-01)

Could automated machine-learned MRI grading aid epidemiological studies of lumbar spinal stenosis? Validation within the Wakayama spine study
od: Yuyu Ishimoto, i wsp.
Wydane: (2020-03-01)

Deep Learning Model for Classifying Metastatic Epidural Spinal Cord Compression on MRI
od: James Thomas Patrick Decourcy Hallinan, i wsp.
Wydane: (2022-05-01)

Automatic multiclass intramedullary spinal cord tumor segmentation on MRI with deep learning
od: Andreanne Lemay, i wsp.
Wydane: (2021-01-01)

Deep learning-based automated segmentation of resection cavities on postsurgical epilepsy MRI
od: T. Campbell Arnold, i wsp.
Wydane: (2022-01-01)

Automated brain tumor recognition using equilibrium optimizer with deep learning approach on MRI images
od: Mahmoud Ragab, i wsp.
Wydane: (2024-11-01)

Trustworthy Deep Learning For The Automated Quantification Of The Fatty Infiltration Of The Rotator Cuff Muscles Using MRI
od: Laith Alzubaidi, i wsp.
Wydane: (2024-11-01)

SpineNet: Automated classification and evidence visualization in spinal MRIs
od: Jamaludin, A, i wsp.
Wydane: (2017)

A deep learning model for detection of cervical spinal cord compression in MRI scans
od: Zamir Merali, i wsp.
Wydane: (2021-05-01)

Automated brain tumor diagnostics: Empowering neuro-oncology with deep learning-based MRI image analysis
od: Subathra Gunasekaran, i wsp.
Wydane: (2024-01-01)

Automated Detection of Spinal Lesions From CT Scans via Deep Transfer Learning
od: Andrea Camisa, i wsp.
Wydane: (2024-01-01)

Fully Automated Deep Learning-Based Renal Mass Detection on Multi-Parametric MRI
od: Rohini Gaikar, i wsp.
Wydane: (2024-01-01)

Towards fully automated segmentation of rat cardiac MRI by leveraging deep learning frameworks
od: Daniel Fernández-Llaneza, i wsp.
Wydane: (2022-06-01)

Automated detection, labelling and radiological grading of clinical spinal MRIs
od: Windsor, R, i wsp.
Wydane: (2024)

Automated detection, labelling and radiological grading of clinical spinal MRIs
od: Rhydian Windsor, i wsp.
Wydane: (2024-07-01)

Benign and malignant diagnosis of spinal tumors based on deep learning and weighted fusion framework on MRI
od: Hong Liu, i wsp.
Wydane: (2022-05-01)

Automated Segmentation of Infarct Lesions in T1-Weighted MRI Scans Using Variational Mode Decomposition and Deep Learning
od: May Phu Paing, i wsp.
Wydane: (2021-03-01)

Automated Detection and Measurement of Dural Sack Cross-Sectional Area in Lumbar Spine MRI Using Deep Learning
od: Babak Saravi, i wsp.
Wydane: (2023-09-01)

Automated 3-dimensional MRI segmentation for the posterosuperior rotator cuff tear lesion using deep learning algorithm.
od: Su Hyun Lee, i wsp.
Wydane: (2023-01-01)

Denoising diffusion-based MRI to CT image translation enables automated spinal segmentation
od: Robert Graf, i wsp.
Wydane: (2023-11-01)

Deep-Learning-Based Automated Rotator Cuff Tear Screening in Three Planes of Shoulder MRI
od: Kyu-Chong Lee, i wsp.
Wydane: (2023-10-01)

MRI radiomics based on deep learning automated segmentation to predict early recurrence of hepatocellular carcinoma
od: Hong Wei, i wsp.
Wydane: (2024-05-01)

Automated analysis of heterogeneous catalyst materials using deep learning
od: Treder, KP
Wydane: (2022)

Automated Diagnosis of Prostate Cancer Using mpMRI Images: A Deep Learning Approach for Clinical Decision Support
od: Anil B. Gavade, i wsp.
Wydane: (2023-07-01)

Deep learning system for automated detection of posterior ligamentous complex injury in patients with thoracolumbar fracture on MRI
od: Sang Won Jo, i wsp.
Wydane: (2023-11-01)

Automated measurement of anteroposterior diameter and foraminal widths in MRI images for lumbar spinal stenosis diagnosis.
od: Friska Natalia, i wsp.
Wydane: (2020-01-01)

Anatomical and functional analysis of cardiac MRI scans using deep learning
od: Xu, H
Wydane: (2020)

A patch-based deep learning MRI segmentation model for improving efficiency and clinical examination of the spinal tumor
od: Weimin Chen, i wsp.
Wydane: (2024-12-01)

Automated Abstractive Text Summarization using Deep Learning
od: Karuna G., i wsp.
Wydane: (2023-01-01)

Automated riverbed composition analysis using deep learning on underwater images
od: A. A. Ermilov, i wsp.
Wydane: (2023-11-01)

Empirical Analysis of Automated Stock Trading Using Deep Reinforcement Learning
od: Minseok Kong, i wsp.
Wydane: (2023-01-01)