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Radiography, Thoracic

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Hierarchical medical image report adversarial generation with hybrid discriminator.

Artificial intelligence in medicine Mar 21, 2024
BACKGROUND AND OBJECTIVES: Generating coherent reports from medical images is an important task for reducing doctors' workload. Unlike traditional image captioning tasks, the task of medical image report generation faces more challenges. Current mode...
Thorax Neural Networks, Computer Deep Learning Image Processing, Computer-Assisted Diagnostic Imaging Datasets as Topic Radiography, Thoracic Humans
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Deep Learning Model for Prediction of Bronchopulmonary Dysplasia in Preterm Infants Using Chest Radiographs.

Journal of imaging informatics in medicine Mar 18, 2024
Bronchopulmonary dysplasia (BPD) is common in preterm infants and may result in pulmonary vascular disease, compromising lung function. This study aimed to employ artificial intelligence (AI) techniques to help physicians accurately diagnose BPD in p...
Female Infant, Newborn Male Humans Retrospective Studies Infant, Premature Radiography, Thoracic Bronchopulmonary Dysplasia Lung Deep Learning
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ConTEXTual Net: A Multimodal Vision-Language Model for Segmentation of Pneumothorax.

Journal of imaging informatics in medicine Mar 14, 2024
Radiology narrative reports often describe characteristics of a patient's disease, including its location, size, and shape. Motivated by the recent success of multimodal learning, we hypothesized that this descriptive text could guide medical image a...
Humans Radiography, Thoracic Pneumothorax Neural Networks, Computer Algorithms Natural Language Processing
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Artificial intelligence-assisted double reading of chest radiographs to detect clinically relevant missed findings: a two-centre evaluation.

European radiology Mar 11, 2024
OBJECTIVES: To evaluate an artificial intelligence (AI)-assisted double reading system for detecting clinically relevant missed findings on routinely reported chest radiographs.
Artificial Intelligence Algorithms Natural Language Processing Retrospective Studies Radiographic Image Interpretation, Computer-Assisted Radiography, Thoracic Female Male Humans Aged Middle Aged Diagnostic Errors Deep Learning
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Evaluation of Effectiveness of Self-Supervised Learning in Chest X-Ray Imaging to Reduce Annotated Images.

Journal of imaging informatics in medicine Mar 8, 2024
A significant challenge in machine learning-based medical image analysis is the scarcity of medical images. Obtaining a large number of labeled medical images is difficult because annotating medical images is a time-consuming process that requires sp...
Radiographic Image Interpretation, Computer-Assisted Supervised Machine Learning Radiography, Thoracic Humans COVID-19 SARS-CoV-2
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Improving Computer-Aided Thoracic Disease Diagnosis through Comparative Analysis Using Chest X-ray Images Taken at Different Times.

Sensors (Basel, Switzerland) Feb 24, 2024
Medical professionals in thoracic medicine routinely analyze chest X-ray images, often comparing pairs of images taken at different times to detect lesions or anomalies in patients. This research aims to design a computer-aided diagnosis system that ...
X-Rays Computers Humans Thoracic Diseases Deep Learning Radiography, Thoracic Neural Networks, Computer Algorithms
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Ultra-low dose chest CT with silver filter and deep learning reconstruction significantly reduces radiation dose and retains quantitative information in the investigation and monitoring of lymphangioleiomyomatosis (LAM).

European radiology Feb 22, 2024
PURPOSE: Frequent CT scans to quantify lung involvement in cystic lung disease increases radiation exposure. Beam shaping energy filters can optimize imaging properties at lower radiation dosages. The aim of this study is to investigate whether use o...
Middle Aged Humans Deep Learning Lung Neoplasms Algorithms Radiography, Thoracic Silver Female Prospective Studies Lymphangioleiomyomatosis Adult Radiographic Image Interpretation, Computer-Assisted Tomography, X-Ray Computed Male Radiation Dosage
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Approximating Intermediate Feature Maps of Self-Supervised Convolution Neural Network to Learn Hard Positive Representations in Chest Radiography.

Journal of imaging informatics in medicine Feb 21, 2024
Recent advances in contrastive learning have significantly improved the performance of deep learning models. In contrastive learning of medical images, dealing with positive representation is sometimes difficult because some strong augmentation techn...
Neural Networks, Computer Deep Learning Radiography, Thoracic Humans
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Automated Detection of Pediatric Foreign Body Aspiration from Chest X-rays Using Machine Learning.

The Laryngoscope Feb 17, 2024
OBJECTIVE/HYPOTHESIS: Standard chest radiographs are a poor diagnostic tool for pediatric foreign body aspiration. Machine learning may improve upon the diagnostic capabilities of chest radiographs. The objective is to develop a machine learning algo...
Retrospective Studies Female Sensitivity and Specificity Male Infant Adolescent Humans Machine Learning Algorithms Foreign Bodies Child, Preschool Respiratory Aspiration Child Radiography, Thoracic
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Differential privacy preserved federated learning for prognostic modeling in COVID-19 patients using large multi-institutional chest CT dataset.

Medical physics Feb 9, 2024
BACKGROUND: Notwithstanding the encouraging results of previous studies reporting on the efficiency of deep learning (DL) in COVID-19 prognostication, clinical adoption of the developed methodology still needs to be improved. To overcome this limitat...
Datasets as Topic COVID-19 Radiography, Thoracic Female Humans Prognosis Middle Aged Aged Deep Learning Tomography, X-Ray Computed Male Privacy
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