AIMC Topic: Lung

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Automatic Lung Segmentation on Chest X-rays Using Self-Attention Deep Neural Network.

Sensors (Basel, Switzerland) Jan 7, 2021
Accurate identification of the boundaries of organs or abnormal objects (e.g., tumors) in medical images is important in surgical planning and in the diagnosis and prognosis of diseases. In this study, we propose a deep learning-based method to segme...
Lung Image Processing, Computer-Assisted Attention Neural Networks, Computer X-Rays
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Prediction of Obstructive Lung Disease from Chest Radiographs via Deep Learning Trained on Pulmonary Function Data.

International journal of chronic obstructive pulmonary disease Jan 5, 2021
BACKGROUND: Chronic obstructive pulmonary disease (COPD), the third leading cause of death worldwide, is often underdiagnosed.
Lung Deep Learning Female Humans Vital Capacity Retrospective Studies Pulmonary Disease, Chronic Obstructive Male
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A new resource on artificial intelligence powered computer automated detection software products for tuberculosis programmes and implementers.

Tuberculosis (Edinburgh, Scotland) Jan 4, 2021
Recently, the number of artificial intelligence powered computer-aided detection (CAD) products that detect tuberculosis (TB)-related abnormalities from chest X-rays (CXR) available on the market has increased. Although CXR is a relatively effective ...
Mycobacterium tuberculosis Diffusion of Innovation Automation Radiographic Image Interpretation, Computer-Assisted Tuberculosis, Pulmonary Host-Pathogen Interactions Radiography, Thoracic Reproducibility of Results Lung Diagnosis, Computer-Assisted Deep Learning Predictive Value of Tests Humans Software
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An efficient method for building a database of diatom populations for drowning site inference using a deep learning algorithm.

International journal of legal medicine Jan 3, 2021
Seasonal or monthly databases of the diatom populations in specific bodies of water are needed to infer the drowning site of a drowned body. However, existing diatom testing methods are laborious, time-consuming, and costly and usually require specif...
China Rats Rats, Sprague-Dawley Animals Lung Diatoms Forensic Pathology Drowning Neural Networks, Computer Seasons Rivers Artificial Intelligence Models, Animal Databases, Factual Sensitivity and Specificity
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ResBCDU-Net: A Deep Learning Framework for Lung CT Image Segmentation.

Sensors (Basel, Switzerland) Jan 3, 2021
Lung CT image segmentation is a key process in many applications such as lung cancer detection. It is considered a challenging problem due to existing similar image densities in the pulmonary structures, different types of scanners, and scanning prot...
Deep Learning Neural Networks, Computer Tomography, X-Ray Computed Lung Humans
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A CT-Based Automated Algorithm for Airway Segmentation Using Freeze-and-Grow Propagation and Deep Learning.

IEEE transactions on medical imaging Dec 29, 2020
Chronic obstructive pulmonary disease (COPD) is a common lung disease, and quantitative CT-based bronchial phenotypes are of increasing interest as a means of exploring COPD sub-phenotypes, establishing disease progression, and evaluating interventio...
Deep Learning Reproducibility of Results Lung Algorithms Tomography, X-Ray Computed
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Spiculation Sign Recognition in a Pulmonary Nodule Based on Spiking Neural P Systems.

BioMed research international Dec 23, 2020
The spiculation sign is one of the main signs to distinguish benign and malignant pulmonary nodules. In order to effectively extract the image feature of a pulmonary nodule for the spiculation sign distinguishment, a new spiculation sign recognition ...
Lung Neoplasms Solitary Pulmonary Nodule Image Interpretation, Computer-Assisted Humans Algorithms Neural Networks, Computer Lung
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Lightweight deep learning models for detecting COVID-19 from chest X-ray images.

Computers in biology and medicine Dec 22, 2020
Deep learning methods have already enjoyed an unprecedented success in medical imaging problems. Similar success has been evidenced when it comes to the detection of COVID-19 from medical images, therefore deep learning approaches are considered good...
COVID-19 Tomography, X-Ray Computed Female SARS-CoV-2 Male Humans Models, Theoretical Lung Deep Learning
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Active contour regularized semi-supervised learning for COVID-19 CT infection segmentation with limited annotations.

Physics in medicine and biology Dec 18, 2020
Infection segmentation on chest CT plays an important role in the quantitative analysis of COVID-19. Developing automatic segmentation tools in a short period with limited labelled images has become an urgent need. Pseudo label-based semi-supervised ...
Tomography, X-Ray Computed Lung Supervised Machine Learning Image Processing, Computer-Assisted Humans COVID-19
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Vulnerability of deep neural networks for detecting COVID-19 cases from chest X-ray images to universal adversarial attacks.

PloS one Dec 17, 2020
Owing the epidemic of the novel coronavirus disease 2019 (COVID-19), chest X-ray computed tomography imaging is being used for effectively screening COVID-19 patients. The development of computer-aided systems based on deep neural networks (DNNs) has...
Lung SARS-CoV-2 Databases, Factual Humans Male Thorax Neural Networks, Computer Tomography, X-Ray Computed COVID-19 Female
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