Machine learning to assess the fetal brain from ultrasound images

Machine learning to assess the fetal brain from ultrasound images

<p>Obstetric care decisions fundamentally rely upon accurate estimation of gestational age (GA). Ultrasound- (US) based measurements provide reliable estimates of GA, if performed early in pregnancy. However, in low-income settings, the lack of appropriately trained sonographers and the tenden...

Bibliográfalaš dieđut
Váldodahkki:	Namburete, A
Eará dahkkit:	Noble, J
Materiálatiipa:	Oahppočájánas
Giella:	English
Almmustuhtton:	2015
Fáttát:	Biomedical engineering Machine learning Image analysis Engineering

Geahča maid

Medical image analysis for simplified ultrasound protocols
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Almmustuhtton: (2023)

Assessment of obstetric ultrasound images using machine learning
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Almmustuhtton: (2012)

Improving deep-learning segmentation performance in 3D neuroimaging with minimal manual annotations
Dahkki: Venturini, L
Almmustuhtton: (2021)

Deep learning techniques to bridge the gap between 2D and 3D ultrasound imaging
Dahkki: Yeung, PH
Almmustuhtton: (2022)

A special section on methods and application in biomedical imaging – part 3
Dahkki: Liu, Feng, et al.
Almmustuhtton: (2019)

Quantifying brain maturation in the preterm baby from EEG sleep analyses
Dahkki: Pillay, K
Almmustuhtton: (2018)

Experimenting with Extreme Learning Machine for Biomedical Image Classification
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Almmustuhtton: (2023-07-01)

Machine Vision based Intelligent Breast Cancer Detection
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Almmustuhtton: (2022-03-01)

A special section on methods and application in biomedical imaging – part 1
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Almmustuhtton: (2019)

A special section on methods and application in biomedical imaging – part 2
Dahkki: Deniz, Luis Gomez, et al.
Almmustuhtton: (2019)

Machine learning for the detection of clinical deterioration on hospital wards
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Almmustuhtton: (2019)

Development of image-based method for the quantitative assessment of cartilage damage from CT as a biomarker in osteoarthritis
Dahkki: Zheng, J-Q
Almmustuhtton: (2024)

Quantification of fetal brain development from ultrasound images using interpretable deep learning
Dahkki: Hesse, LS
Almmustuhtton: (2023)

Prediction of fetal blood pressure during labour with deep learning techniques
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Almmustuhtton: (2023)

Prenatal assessment of placenta accreta spectrum disorders from ultrasound images using deep learning
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Almmustuhtton: (2019)

On-chip generation of microbubbles in photoacoustic contrast agents for dual modal ultrasound/photoacoustic in vivo animal imaging
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Almmustuhtton: (2018)

Segmentation and sizing of breast cancer masses with ultrasound elasticity imaging
Dahkki: von Lavante, E
Almmustuhtton: (2009)

Ultrasound parametric imaging and image analysis for breast cancer characterisation and treatment monitoring
Dahkki: Chuang, B
Almmustuhtton: (2014)

Automatic measurements of femoral characteristics using 3D ultrasound images in utero
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Almmustuhtton: (2011)

A new technique for microbubble characterisation and the implications to contrast enhanced ultrasound
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Almmustuhtton: (2016)

Biomedical Applications of Infrared Thermal Imaging: Current State of Machine Learning Classification
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Almmustuhtton: (2019-10-01)

Detection of epileptic seizures in EEG by using machine learning techniques
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Robust deep learning methods for accountable contrast-agent-free CMR imaging in clinical applications
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Diameter-dependent assessment of microvascular leakage following ultrasound-mediated blood-brain barrier opening
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Almmustuhtton: (2023-06-01)

Generation of 12-lead patient-specific electrocardiogram and risk prediction of cardiovascular disease using deep learning
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Almmustuhtton: (2023)

Automated feature extraction on brain MRI images for predicting multiple sclerosis patient disability
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Almmustuhtton: (2022)

Explainable machine learning of the breast cancer staging for designing smart biomarker sensors
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Spectral analysis of breast ultrasound data with application to mass sizing and characterization
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Almmustuhtton: (2014)

Machine Learning in Bio-Signal Analysis and Diagnostic Imaging /
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Almmustuhtton: (2019)

Time-efficient methods for non-invasive brain blood flow imaging using arterial spin labelling MRI
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Almmustuhtton: (2019)

Connectivity driven registration of magnetic resonance images of the human brain
Dahkki: Petrović, A
Almmustuhtton: (2010)

Computer-aided analysis of fetal cardiac ultrasound videos
Dahkki: Bridge, C
Almmustuhtton: (2017)

MCI progression classification for early diagnosis of Alzheimer’s disease using machine learning and deep learning methods
Dahkki: Yan, Y
Almmustuhtton: (2020)

Investigating magnetically targeted microbubbles for ultrasound-enhanced thrombolysis
Dahkki: de Saint Victor, M
Almmustuhtton: (2016)

Development of biomedical image processing software package for new learners /
Dahkki: Lee, Chen Hsieng, 1988-, et al.
Almmustuhtton: (2012)

Coherent photoacoustic-ultrasound correlation and imaging
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Almmustuhtton: (2014)

Quantitative histopathological assessment of liver fibrosis
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Almmustuhtton: (2023)

Development of biomedical image processing software package for new learners [electronic resources] /
Dahkki: Lee, Chen Hsieng, 1988-, et al.
Almmustuhtton: (2012)

Learning to study the developing brain: characterising brain development with machine learning, image analysis and ex vivo culture
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Almmustuhtton: (2018)

Use of high intensity focused ultrasound to destroy subcutaneous fat tissue
Dahkki: Kyriakou, Z
Almmustuhtton: (2010)