AIMC Topic: Thorax

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Multi-task learning for the segmentation of organs at risk with label dependence.

Medical image analysis Feb 7, 2020
Automatic segmentation of organs at risk is crucial to aid diagnoses and remains a challenging task in medical image analysis domain. To perform the segmentation, we use multi-task learning (MTL) to accurately determine the contour of organs at risk ...
Organs at Risk Thorax Radiotherapy Humans Neural Networks, Computer Tomography, X-Ray Computed
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Label Co-Occurrence Learning With Graph Convolutional Networks for Multi-Label Chest X-Ray Image Classification.

IEEE journal of biomedical and health informatics Jan 16, 2020
Existing multi-label medical image learning tasks generally contain rich relationship information among pathologies such as label co-occurrence and interdependency, which is of great importance for assisting in clinical diagnosis and can be represent...
Neural Networks, Computer Databases, Factual Lung Diseases Humans Data Curation Image Interpretation, Computer-Assisted Radiography, Thoracic Thorax
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Automatic multi-organ segmentation in dual-energy CT (DECT) with dedicated 3D fully convolutional DECT networks.

Medical physics Jan 1, 2020
PURPOSE: Dual-energy computed tomography (DECT) has shown great potential in many clinical applications. By incorporating the information from two different energy spectra, DECT provides higher contrast and reveals more material differences of tissue...
Thorax Dose-Response Relationship, Radiation Radiography, Dual-Energy Scanned Projection Liver Signal-To-Noise Ratio Spleen Lung Humans Deep Learning Tomography, X-Ray Computed Image Processing, Computer-Assisted Kidney Abdomen Models, Theoretical
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Artificial intelligence applications for thoracic imaging.

European journal of radiology Dec 11, 2019
Artificial intelligence is a hot topic in medical imaging. The development of deep learning methods and in particular the use of convolutional neural networks (CNNs), have led to substantial performance gain over the classic machine learning techniqu...
Lung Neoplasms Early Detection of Cancer Radiologists Machine Learning Algorithms Deep Learning Radiography, Thoracic Thorax Humans Artificial Intelligence
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Deep Learning for Chest Radiography in the Emergency Department.

Radiology Oct 22, 2019
Deep Learning Radiography Emergency Service, Hospital Thorax
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How many models/atlases are needed as priors for capturing anatomic population variations?

Medical image analysis Sep 3, 2019
Many medical image processing and analysis operations can benefit a great deal from prior information encoded in the form of models/atlases to capture variations over a population in form, shape, anatomic layout, and image appearance of objects. Howe...
Humans Tomography, X-Ray Computed Thorax Deep Learning Atlases as Topic Image Processing, Computer-Assisted Neck Anatomic Variation Datasets as Topic Head
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Synthesis of Electrocardiogram V-Lead Signals From Limb-Lead Measurement Using R-Peak Aligned Generative Adversarial Network.

IEEE journal of biomedical and health informatics Aug 21, 2019
Recently, portable electrocardiogram (ECG) hardware devices have been developed using limb-lead measurements. However, portable ECGs provide insufficient ECG information because of limitations in the number of leads and measurement positions. Therefo...
Thorax Extremities Signal Processing, Computer-Assisted Heart Heart Diseases Humans Machine Learning Electrocardiography
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Detecting Mistakes in CPR Training with Multimodal Data and Neural Networks.

Sensors (Basel, Switzerland) Jul 13, 2019
This study investigated to what extent multimodal data can be used to detect mistakes during Cardiopulmonary Resuscitation (CPR) training. We complemented the Laerdal QCPR ResusciAnne manikin with the Multimodal Tutor for CPR, a multi-sensor system c...
Thorax Humans Data Curation Neural Networks, Computer Surveys and Questionnaires Cardiopulmonary Resuscitation Databases, Factual Posture Body Weight Equipment Design Information Storage and Retrieval Education, Medical Manikins Computer-Assisted Instruction
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Deep Learning to Assess Long-term Mortality From Chest Radiographs.

JAMA network open Jul 3, 2019
IMPORTANCE: Chest radiography is the most common diagnostic imaging test in medicine and may also provide information about longevity and prognosis.
Deep Learning Mass Screening Lung Diseases Clinical Trials as Topic Cardiovascular Diseases Neural Networks, Computer Middle Aged Radiography, Thoracic Risk Factors Prognosis Risk Assessment Thorax Aged Female Male Humans
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Development of a deep neural network for generating synthetic dual-energy chest x-ray images with single x-ray exposure.

Physics in medicine and biology May 31, 2019
Dual-energy chest radiography (DECR) is a medical imaging technology that can improve diagnostic accuracy. This technique can decompose single-energy chest radiography (SECR) images into separate bone- and soft tissue-only images. This can, however, ...
Radiography Humans Thorax Deep Learning Radiation Exposure Image Processing, Computer-Assisted
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