AIMC Topic: Lung

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Automated Lung Segmentation on Chest Computed Tomography Images with Extensive Lung Parenchymal Abnormalities Using a Deep Neural Network.

Korean journal of radiology Oct 30, 2020
OBJECTIVE: We aimed to develop a deep neural network for segmenting lung parenchyma with extensive pathological conditions on non-contrast chest computed tomography (CT) images.
Deep Learning Lung Diseases, Interstitial Humans Automation Databases, Factual Tomography, X-Ray Computed Female Male Middle Aged Lung Aged Aged, 80 and over Adult
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The importance of standardisation - COVID-19 CT & Radiograph Image Data Stock for deep learning purpose.

Computers in biology and medicine Oct 28, 2020
With the number of affected individuals still growing world-wide, the research on COVID-19 is continuously expanding. The deep learning community concentrates their efforts on exploring if neural networks can potentially support the diagnosis using C...
Tomography, X-Ray Computed SARS-CoV-2 Humans COVID-19 Lung Deep Learning Radiographic Image Interpretation, Computer-Assisted
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Identifying gross post-mortem organ images using a pre-trained convolutional neural network.

Journal of forensic sciences Oct 26, 2020
Identifying organs/tissue and pathology on radiological and microscopic images can be performed using convolutional neural networks (CNN). However, there are scant studies on applying CNN to post-mortem gross images of visceral organs. This proof-of-...
Spleen Photography Forensic Pathology Neural Networks, Computer Brain Lung Liver Myocardium Image Processing, Computer-Assisted Kidney Humans
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Lung Shrinkage: An Additional CT Marker in the Follow-up of Fibrotic Interstitial Lung Disease.

Radiology Oct 20, 2020
Humans Tomography, X-Ray Computed Lung Deep Learning Follow-Up Studies Lung Diseases, Interstitial Scleroderma, Systemic
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Elastic Registration-driven Deep Learning for Longitudinal Assessment of Systemic Sclerosis Interstitial Lung Disease at CT.

Radiology Oct 20, 2020
Background Longitudinal follow-up of interstitial lung diseases (ILDs) at CT mainly relies on the evaluation of the extent of ILD, without accounting for lung shrinkage. Purpose To develop a deep learning-based method to depict worsening of ILD based...
Humans Retrospective Studies Tomography, X-Ray Computed Female Scleroderma, Systemic Deep Learning Longitudinal Studies Radiographic Image Interpretation, Computer-Assisted Lung Diseases, Interstitial Male Middle Aged Lung
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Chest x-ray analysis with deep learning-based software as a triage test for pulmonary tuberculosis: a prospective study of diagnostic accuracy for culture-confirmed disease.

The Lancet. Digital health Oct 19, 2020
BACKGROUND: Deep learning-based radiological image analysis could facilitate use of chest x-rays as triage tests for pulmonary tuberculosis in resource-limited settings. We sought to determine whether commercially available chest x-ray analysis softw...
Radiology Sputum Pakistan Tuberculosis, Pulmonary Body Mass Index Sensitivity and Specificity Age Factors Female Triage Male Young Adult HIV Infections Middle Aged Mycobacterium tuberculosis Adult Lung Image Processing, Computer-Assisted Sex Factors Humans Deep Learning X-Rays Software Prospective Studies
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Findings from machine learning in clinical medical imaging applications - Lessons for translation to the forensic setting.

Forensic science international Oct 18, 2020
Machine learning (ML) techniques are increasingly being used in clinical medical imaging to automate distinct processing tasks. In post-mortem forensic radiology, the use of these algorithms presents significant challenges due to variability in organ...
Forensic Medicine Bone and Bones Lung Support Vector Machine Diagnostic Imaging Neural Networks, Computer Machine Learning Algorithms Brain Humans
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CoLe-CNN: Context-learning convolutional neural network with adaptive loss function for lung nodule segmentation.

Computer methods and programs in biomedicine Oct 15, 2020
BACKGROUND AND OBJECTIVE: An accurate segmentation of lung nodules in computed tomography images is a crucial step for the physical characterization of the tumour. Being often completely manually accomplished, nodule segmentation turns to be a tediou...
Tomography, X-Ray Computed Lung Humans Algorithms Neural Networks, Computer Databases, Factual
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Extracting Lungs from CT Images via Deep Convolutional Neural Network Based Segmentation and Two-Pass Contour Refinement.

Journal of digital imaging Oct 15, 2020
Lung segmentation is a key step of thoracic computed tomography (CT) image processing, and it plays an important role in computer-aided pulmonary disease diagnostics. However, the presence of image noises, pathologies, vessels, individual anatomical ...
Humans Neural Networks, Computer Lung Image Processing, Computer-Assisted Algorithms Tomography, X-Ray Computed
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A comprehensive study on classification of COVID-19 on computed tomography with pretrained convolutional neural networks.

Scientific reports Oct 9, 2020
The use of imaging data has been reported to be useful for rapid diagnosis of COVID-19. Although computed tomography (CT) scans show a variety of signs caused by the viral infection, given a large amount of images, these visual features are difficult...
Pandemics COVID-19 Databases, Factual Tomography, X-Ray Computed Humans Neural Networks, Computer Artificial Intelligence Data Management Coronavirus Infections Betacoronavirus Pneumonia, Viral SARS-CoV-2 Lung Image Processing, Computer-Assisted
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