AIMC Topic: Radiography

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Beyond the : Potential of Deep Learning Image Classification for Chest Radiography.

AJR. American journal of roentgenology Sep 15, 2021
Radiography Radiography, Thoracic Humans Deep Learning
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Use of Artificial Intelligence-Based Software as Medical Devices for Chest Radiography: A Position Paper from the Korean Society of Thoracic Radiology.

Korean journal of radiology Sep 13, 2021
Humans Artificial Intelligence Radiography, Thoracic Radiography Radiology Software Republic of Korea
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Automatic detection of actionable radiology reports using bidirectional encoder representations from transformers.

BMC medical informatics and decision making Sep 11, 2021
BACKGROUND: It is essential for radiologists to communicate actionable findings to the referring clinicians reliably. Natural language processing (NLP) has been shown to help identify free-text radiology reports including actionable findings. However...
Humans Radiography Logistic Models Radiology Machine Learning Natural Language Processing
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Integrating artificial intelligence into radiology practice: undergraduate students' perspective.

European journal of nuclear medicine and molecular imaging Sep 10, 2021
Students Surveys and Questionnaires Radiography Radiology Humans Artificial Intelligence
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Multitask Deep Learning for Segmentation and Classification of Primary Bone Tumors on Radiographs.

Radiology Sep 7, 2021
Background An artificial intelligence model that assesses primary bone tumors on radiographs may assist in the diagnostic workflow. Purpose To develop a multitask deep learning (DL) model for simultaneous bounding box placement, segmentation, and cla...
Female Adult Humans Retrospective Studies Bone Neoplasms Radiography Radiographic Image Interpretation, Computer-Assisted Male Deep Learning Bone and Bones
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Detecting hip osteoarthritis on clinical CT: a deep learning application based on 2-D summation images derived from CT.

Osteoporosis international : a journal established as result of cooperation between the European Foundation for Osteoporosis and the National Osteoporosis Foundation of the USA Sep 2, 2021
UNLABELLED: We developed and compared deep learning models to detect hip osteoarthritis on clinical CT. The CT-based summation images, CT-AP, that resemble X-ray radiographs can detect radiographic hip osteoarthritis and in the absence of large train...
Deep Learning Retrospective Studies ROC Curve Tomography, X-Ray Computed Radiography Humans Osteoarthritis, Hip
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Visual Analytics for Hypothesis-Driven Exploration in Computational Pathology.

IEEE transactions on visualization and computer graphics Sep 1, 2021
Recent advances in computational and algorithmic power are evolving the field of medical imaging rapidly. In cancer research, many new directions are sought to characterize patients with additional imaging features derived from radiology and patholog...
Algorithms Female Humans Image Interpretation, Computer-Assisted Machine Learning Histological Techniques Breast Neoplasms Imaging Genomics Radiography
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A multiphase texture-based model of active contours assisted by a convolutional neural network for automatic CT and MRI heart ventricle segmentation.

Computer methods and programs in biomedicine Aug 31, 2021
BACKGROUND: Left and right ventricle automatic segmentation remains one of the more important tasks in computed aided diagnosis. Active contours have shown to be efficient for this task, however they often require user interaction to provide the init...
Radiography Image Processing, Computer-Assisted Neural Networks, Computer Heart Ventricles Magnetic Resonance Imaging Tomography, X-Ray Computed
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Contralaterally Enhanced Networks for Thoracic Disease Detection.

IEEE transactions on medical imaging Aug 31, 2021
Identifying and locating diseases in chest X-rays are very challenging, due to the low visual contrast between normal and abnormal regions, and distortions caused by other overlapping tissues. An interesting phenomenon is that there exist many simila...
Thorax Thoracic Diseases Humans Deep Learning Lung Radiography
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Balanced Convolutional Neural Networks for Pneumoconiosis Detection.

International journal of environmental research and public health Aug 28, 2021
Pneumoconiosis remains one of the most common and harmful occupational diseases in China, leading to huge economic losses to society with its high prevalence and costly treatment. Diagnosis of pneumoconiosis still strongly depends on the experience o...
Pneumoconiosis Radiography Machine Learning China Humans Neural Networks, Computer
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