Radiology

Latest AI and machine learning research in radiology for healthcare professionals.

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Artificial intelligence to diagnose meniscus tears on MRI.

PURPOSE: The purpose of this study was to build and evaluate a high-performance algorithm to detect ...

Mar 2019 30928472
Kidney cortex segmentation in 2D CT with U-Nets ensemble aggregation.

PURPOSE: This work presents our contribution to one of the data challenges organized by the French R...

Mar 2019 30926445
Weakly Supervised Deep Learning for Brain Disease Prognosis Using MRI and Incomplete Clinical Scores.

As a hot topic in brain disease prognosis, predicting clinical measures of subjects based on brain m...

Mar 2019 30932861
Deep Learning for Automated Contouring of Primary Tumor Volumes by MRI for Nasopharyngeal Carcinoma.

Background Nasopharyngeal carcinoma (NPC) may be cured with radiation therapy. Tumor proximity to cr...

Mar 2019 30912722
Detection and characterization of MRI breast lesions using deep learning.

PURPOSE: The purpose of this study was to assess the potential of a deep learning model to discrimin...

Mar 2019 30926444
A Technical Review of Convolutional Neural Network-Based Mammographic Breast Cancer Diagnosis.

This study reviews the technique of convolutional neural network (CNN) applied in a specific field o...

Mar 2019 31019547
3D whole brain segmentation using spatially localized atlas network tiles.

Detailed whole brain segmentation is an essential quantitative technique in medical image analysis, ...

Mar 2019 30910724
Arterial Spin Labeling Images Synthesis From sMRI Using Unbalanced Deep Discriminant Learning.

Adequate medical images are often indispensable in contemporary deep learning-based medical imaging ...

Mar 2019 30908201
Prediction of molecular subtypes of breast cancer using BI-RADS features based on a "white box" machine learning approach in a multi-modal imaging setting.

PURPOSE: To develop and validate an interpretable and repeatable machine learning model approach to ...

Mar 2019 31005170
Unsupervised domain adaptation for medical imaging segmentation with self-ensembling.

Recent advances in deep learning methods have redefined the state-of-the-art for many medical imagin...

Mar 2019 30898655
Governance of automated image analysis and artificial intelligence analytics in healthcare.

The hype over artificial intelligence (AI) has spawned claims that clinicians (particularly radiolog...

Mar 2019 30898383
Brain Morphometry Methods for Feature Extraction in Random Subspace Ensemble Neural Network Classification of First-Episode Schizophrenia.

Machine learning (ML) is a growing field that provides tools for automatic pattern recognition. The ...

Mar 2019 30883275
Artificial intelligence in breast imaging.

This article reviews current limitations and future opportunities for the application of computer-ai...

Mar 2019 30898381
A novel end-to-end brain tumor segmentation method using improved fully convolutional networks.

Accurate brain magnetic resonance imaging (MRI) tumor segmentation continues to be an active researc...

Mar 2019 31005007
Modeling second-order boundary perception: A machine learning approach.

Visual pattern detection and discrimination are essential first steps for scene analysis. Numerous h...

Mar 2019 30883556
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 objec...

Mar 2019 30885592
Incorporating prior knowledge via volumetric deep residual network to optimize the reconstruction of sparsely sampled MRI.

For sparse sampling that accelerates magnetic resonance (MR) image acquisition, non-linear reconstru...

Mar 2019 30880112
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 capab...

Mar 2019 30868478
Technical Note: Deriving ventilation imaging from 4DCT by deep convolutional neural network.

PURPOSE: Ventilation images can be derived from four-dimensional computed tomography (4DCT) by analy...

Mar 2019 30714159
Automated pectoral muscle identification on MLO-view mammograms: Comparison of deep neural network to conventional computer vision.

OBJECTIVES: The aim of this study was to develop a fully automated deep learning approach for identi...

Mar 2019 30771257
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