Pregnancy

Automatic classification of ultrasound breast lesions using a deep convolutional neural network mimicking human decision-making.

OBJECTIVES: To evaluate a deep convolutional neural network (dCNN) for detection, highlighting, and classification of ultrasound (US) breast lesions mimicking human decision-making according to the Breast Imaging Reporting and Data System (BI-RADS).

Latent Phase Detection of Hypoxic-Ischemic Spike Transients in the EEG of Preterm Fetal Sheep Using Reverse Biorthogonal Wavelets & Fuzzy Classifier.

Hypoxic-ischemic (HI) studies in preterms lack reliable prognostic biomarkers for diagnostic tests of HI encephalopathy (HIE). Our group's observations from fetal sheep models suggest that potential biomarkers of HIE in the form of developing HI micro-scale epileptiform transients emerge along suppressed EEG/ECoG background during a latent phase of 6-7h post-insult. However, having to observe for...

Robust segmentation of arterial walls in intravascular ultrasound images using Dual Path U-Net.

A Fully Convolutional Network (FCN) based deep architecture called Dual Path U-Net (DPU-Net) is proposed for automatic segmentation of the lumen and m...

Swarm-Fuzzy Rule-Based Targeted Nano Delivery Using Bioinspired Nanomachines.

Cooperative navigation and swarm decision making take center stage in a broad range of distributed applications with high-environmental and measuremen...

Distinction between benign and malignant breast masses at breast ultrasound using deep learning method with convolutional neural network.

PURPOSE: We aimed to use deep learning with convolutional neural network (CNN) to discriminate between benign and malignant breast mass images from ul...

Prediction of fetal state from the cardiotocogram recordings using neural network models.

The combination of machine vision and soft computing approaches in the clinical decisions, using training data, can improve medical decisions and trea...

Five simultaneous artificial intelligence data challenges on ultrasound, CT, and MRI.

PURPOSE: The goal of this data challenge was to create a structured dynamic with the following objectives: (1) teach radiologists the new rules of Gen...

Automatic segmentation of bone surfaces from ultrasound using a filter-layer-guided CNN.

PURPOSE: Ultrasound (US) provides real-time, two-/three-dimensional safe imaging. Due to these capabilities, it is considered a safe alternative to in...

Beamforming and Speckle Reduction Using Neural Networks.

With traditional beamforming methods, ultrasound B-mode images contain speckle noise caused by the random interference of subresolution scatterers. In...

DeephESC 2.0: Deep Generative Multi Adversarial Networks for improving the classification of hESC.

Human embryonic stem cells (hESC), derived from the blastocysts, provide unique cellular models for numerous potential applications. They have great p...

Decision Fusion-Based Fetal Ultrasound Image Plane Classification Using Convolutional Neural Networks.

Machine learning for ultrasound image analysis and interpretation can be helpful in automated image classification in large-scale retrospective analys...

Using natural language processing and machine learning to classify health literacy from secure messages: The ECLIPPSE study.

Limited health literacy is a barrier to optimal healthcare delivery and outcomes. Current measures requiring patients to self-report limitations are t...

Training improvements for ultrasound beamforming with deep neural networks.

This paper investigates practical considerations of training ultrasound deep neural network (DNN) beamformers. First, we studied training DNNs using t...

Evaluating reinforcement learning agents for anatomical landmark detection.

Automatic detection of anatomical landmarks is an important step for a wide range of applications in medical image analysis. Manual annotation of land...

Quantifying lung ultrasound comets with a convolutional neural network: Initial clinical results.

Lung ultrasound comets are "comet-tail" artifacts appearing in lung ultrasound images. They are particularly useful in detecting several lung patholog...

The use of artificial neural network analysis can improve the risk-stratification of patients presenting with suspected deep vein thrombosis.

Artificial neural networks are machine-learning algorithms designed to analyse data without a pre-existing hypothesis as to any associations that may ...

Attention gated networks: Learning to leverage salient regions in medical images.

We propose a novel attention gate (AG) model for medical image analysis that automatically learns to focus on target structures of varying shapes and ...

Deep convolutional neural network for the diagnosis of thyroid nodules on ultrasound.

BACKGROUND: We designed a deep convolutional neural network (CNN) to diagnose thyroid malignancy on ultrasound (US) and compared the diagnostic perfor...

Gain determination of feedback force for an ultrasound scanning robot using genetic algorithm.

PURPOSE: The remote medical diagnosis system (RMDS) is for providing medical diagnosis to the patients located in remote sites. To apply to RMDS and m...

Argentinian multicenter study on urinary tract infections due to Streptococcus agalactiae in adult patients.

INTRODUCTION: Streptococcus agalactiae (group B streptococcus, GBS) is a recognized urinary pathogen both in males and pregnant or non-pregnant women....