AI Medical Compendium Topic:
Ultrasonography

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Global guidance network for breast lesion segmentation in ultrasound images.

Medical image analysis
Automatic breast lesion segmentation in ultrasound helps to diagnose breast cancer, which is one of the dreadful diseases that affect women globally. Segmenting breast regions accurately from ultrasound image is a challenging task due to the inherent...

Mutual Information-Based Disentangled Neural Networks for Classifying Unseen Categories in Different Domains: Application to Fetal Ultrasound Imaging.

IEEE transactions on medical imaging
Deep neural networks exhibit limited generalizability across images with different entangled domain features and categorical features. Learning generalizable features that can form universal categorical decision boundaries across domains is an intere...

On the identification of thyroid nodules using semi-supervised deep learning.

International journal for numerical methods in biomedical engineering
Detecting malign cases from thyroid nodule examinations is crucial in healthcare particularly to improve the early detection of such cases. However, malign thyroid nodules can be extremely rare and is hard to find using the traditional rule based or ...

Automatic Lumen Border Detection in IVUS Images Using Deep Learning Model and Handcrafted Features.

Ultrasonic imaging
In the clinical analysis of Intravascular ultrasound (IVUS) images, the lumen size is an important indicator of coronary atherosclerosis, and is also the premise of coronary artery disease diagnosis and interventional treatment. In this study, a full...

Preoperative classification of primary and metastatic liver cancer via machine learning-based ultrasound radiomics.

European radiology
OBJECTIVE: To investigate the application of machine learning-based ultrasound radiomics in preoperative classification of primary and metastatic liver cancer.

Classification of malignant tumours in breast ultrasound using unsupervised machine learning approaches.

Scientific reports
Traditional computer-aided diagnosis (CAD) processes include feature extraction, selection, and classification. Effective feature extraction in CAD is important in improving the classification's performance. We introduce a machine-learning method and...

Automatic segmentation of ventricular volume by 3D ultrasonography in post haemorrhagic ventricular dilatation among preterm infants.

Scientific reports
To train, evaluate, and validate the application of a deep learning framework in three-dimensional ultrasound (3D US) for the automatic segmentation of ventricular volume in preterm infants with post haemorrhagic ventricular dilatation (PHVD). We tra...

Ultrasound volume projection image quality selection by ranking from convolutional RankNet.

Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society
Periodic inspection and assessment are important for scoliosis patients. 3D ultrasound imaging has become an important means of scoliosis assessment as it is a real-time, cost-effective and radiation-free imaging technique. With the generation of a 3...

Ultrasound-based internal carotid artery plaque characterization using deep learning paradigm on a supercomputer: a cardiovascular disease/stroke risk assessment system.

The international journal of cardiovascular imaging
Visual or manual characterization and classification of atherosclerotic plaque lesions are tedious, error-prone, and time-consuming. The purpose of this study is to develop and design an automated carotid plaque characterization and classification sy...

Automated ultrasound assessment of amniotic fluid index using deep learning.

Medical image analysis
The estimation of antenatal amniotic fluid (AF) volume (AFV) is important as it offers crucial information about fetal development, fetal well-being, and perinatal prognosis. However, AFV measurement is cumbersome and patient specific. Moreover, it i...