Pregnancy

Super-Resolution Ultrasound Localization Microscopy Through Deep Learning.

Ultrasound localization microscopy has enabled super-resolution vascular imaging through precise localization of individual ultrasound contrast agents (microbubbles) across numerous imaging frames. However, analysis of high-density regions with significant overlaps among the microbubble point spread responses yields high localization errors, constraining the technique to low-concentration conditio...

Semi-supervised GAN-based Radiomics Model for Data Augmentation in Breast Ultrasound Mass Classification.

BACKGROUND AND OBJECTIVE: The capability of deep learning radiomics (DLR) to extract high-level medical imaging features has promoted the use of computer-aided diagnosis of breast mass detected on ultrasound. Recently, generative adversarial network (GAN) has aided in tackling a general issue in DLR, i.e., obtaining a sufficient number of medical images. However, GAN methods require a pair of inpu...

An Evaluation of the Effectiveness of Image-based Texture Features Extracted from Static B-mode Ultrasound Images in Distinguishing between Benign and Malignant Ovarian Masses.

Significant successes in machine learning approaches to image analysis for various applications have energized strong interest in automated diagnostic...

Ultrasound image reconstruction from plane wave radio-frequency data by self-supervised deep neural network.

Image reconstruction from radio-frequency (RF) data is crucial for ultrafast plane wave ultrasound (PWUS) imaging. Compared with the traditional delay...

Deep Learning for Automatic Segmentation of Hybrid Optoacoustic Ultrasound (OPUS) Images.

The highly complementary information provided by multispectral optoacoustics and pulse-echo ultrasound have recently prompted development of hybrid im...

Ultrasound-based deep learning radiomics in the assessment of pathological complete response to neoadjuvant chemotherapy in locally advanced breast cancer.

PURPOSE: The aim of the study was to develop and validate a deep learning radiomic nomogram (DLRN) for preoperatively assessing breast cancer patholog...

A deep learning-based system for bile duct annotation and station recognition in linear endoscopic ultrasound.

BACKGROUND: Detailed evaluation of bile duct (BD) is main focus during endoscopic ultrasound (EUS). The aim of this study was to develop a system for ...

Integrated Learning: Screening Optimal Biomarkers for Identifying Preeclampsia in Placental mRNA Samples.

Preeclampsia (PE) is a maternal disease that causes maternal and child death. Treatment and preventive measures are not sound enough. The problem of P...

The capabilities of nanoelectronic 2-D materials for bio-inspired computing and drug delivery indicate their significance in modern drug design.

Remarkable advancements in the computational techniques and nanoelectronics have attracted considerable interests for development of highly-sophistica...

Intrahepatic cholestasis of pregnancy: machine-learning algorithm to predict elevated bile acid based on clinical and laboratory data.

PURPOSE: Applying machine-learning models to clinical and laboratory features of women with intrahepatic cholestasis of pregnancy (ICP) and creating a...

Automatic segmentation and location learning of neonatal cerebral ventricles in 3D ultrasound data combining CNN and CPPN.

Preterm neonates are highly likely to suffer from ventriculomegaly, a dilation of the Cerebral Ventricular System (CVS). This condition can develop in...

Learning to map 2D ultrasound images into 3D space with minimal human annotation.

In fetal neurosonography, aligning two-dimensional (2D) ultrasound scans to their corresponding plane in the three-dimensional (3D) space remains a ch...

A deep learning methodology for the automated detection of end-diastolic frames in intravascular ultrasound images.

Coronary luminal dimensions change during the cardiac cycle. However, contemporary volumetric intravascular ultrasound (IVUS) analysis is performed in...

Machine learning improves early prediction of small-for-gestational-age births and reveals nuchal fold thickness as unexpected predictor.

OBJECTIVE: To investigate the performance of the machine learning (ML) model in predicting small-for-gestational-age (SGA) at birth, using second-trim...

Global guidance network for breast lesion segmentation in ultrasound images.

Automatic breast lesion segmentation in ultrasound helps to diagnose breast cancer, which is one of the dreadful diseases that affect women globally. ...

Biohybrid robotics: From the nanoscale to the macroscale.

Biohybrid robotics is a field in which biological entities are combined with artificial materials in order to obtain improved performance or features ...

CA-Net: Comprehensive Attention Convolutional Neural Networks for Explainable Medical Image Segmentation.

Accurate medical image segmentation is essential for diagnosis and treatment planning of diseases. Convolutional Neural Networks (CNNs) have achieved ...

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

Deep neural networks exhibit limited generalizability across images with different entangled domain features and categorical features. Learning genera...

Integrated analysis of multiple microarray studies to identify novel gene signatures in preeclampsia.

INTRODUCTION: Preeclampsia (PE) is one of the major causes of maternal and fetal morbidity and mortality in pregnancy worldwide. However, the intrinsi...

Fetal birthweight prediction with measured data by a temporal machine learning method.

BACKGROUND: Birthweight is an important indicator during the fetal development process to protect the maternal and infant safety. However, birthweight...