AIMC Topic: Lung
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Towards radiologist-level cancer risk assessment in CT lung screening using deep learning.
Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society
Mar 5, 2021
PURPOSE: Lung cancer is the leading cause of cancer mortality in the US, responsible for more deaths than breast, prostate, colon and pancreas cancer combined and large population studies have indicated that low-dose computed tomography (CT) screenin...
Development of a convolutional neural network to differentiate among the etiology of similar appearing pathological B lines on lung ultrasound: a deep learning study.
BMJ open
Mar 5, 2021
OBJECTIVES: Lung ultrasound (LUS) is a portable, low-cost respiratory imaging tool but is challenged by user dependence and lack of diagnostic specificity. It is unknown whether the advantages of LUS implementation could be paired with deep learning ...
MAMA Net: Multi-Scale Attention Memory Autoencoder Network for Anomaly Detection.
IEEE transactions on medical imaging
Mar 2, 2021
Anomaly detection refers to the identification of cases that do not conform to the expected pattern, which takes a key role in diverse research areas and application domains. Most of existing methods can be summarized as anomaly object detection-base...
Lung protective ventilation strategy to reduce postoperative pulmonary complications (PPCs) in patients undergoing robot-assisted laparoscopic radical cystectomy for bladder cancer: A randomized double blinded clinical trial.
Journal of clinical anesthesia
Mar 1, 2021
STUDY OBJECTIVE: To evaluate the effects of ventilation with low tidal volume and positive end-expiratory pressure (PEEP) on postoperative pulmonary complications in patients undergoing robot-assisted laparoscopic radical cystectomy (RARC) for bladde...
Anam-Net: Anamorphic Depth Embedding-Based Lightweight CNN for Segmentation of Anomalies in COVID-19 Chest CT Images.
IEEE transactions on neural networks and learning systems
Mar 1, 2021
Chest computed tomography (CT) imaging has become indispensable for staging and managing coronavirus disease 2019 (COVID-19), and current evaluation of anomalies/abnormalities associated with COVID-19 has been performed majorly by the visual score. T...
Accurately Discriminating COVID-19 from Viral and Bacterial Pneumonia According to CT Images Via Deep Learning.
Interdisciplinary sciences, computational life sciences
Feb 27, 2021
Computed tomography (CT) is one of the most efficient diagnostic methods for rapid diagnosis of the widespread COVID-19. However, reading CT films brings a lot of concentration and time for doctors. Therefore, it is necessary to develop an automatic ...
Prediction of Patient Management in COVID-19 Using Deep Learning-Based Fully Automated Extraction of Cardiothoracic CT Metrics and Laboratory Findings.
Korean journal of radiology
Feb 24, 2021
OBJECTIVE: To extract pulmonary and cardiovascular metrics from chest CTs of patients with coronavirus disease 2019 (COVID-19) using a fully automated deep learning-based approach and assess their potential to predict patient management.
Development and Validation of Machine Learning-based Model for the Prediction of Malignancy in Multiple Pulmonary Nodules: Analysis from Multicentric Cohorts.
Clinical cancer research : an official journal of the American Association for Cancer Research
Feb 24, 2021
PURPOSE: Nodule evaluation is challenging and critical to diagnose multiple pulmonary nodules (MPNs). We aimed to develop and validate a machine learning-based model to estimate the malignant probability of MPNs to guide decision-making.
JCS: An Explainable COVID-19 Diagnosis System by Joint Classification and Segmentation.
IEEE transactions on image processing : a publication of the IEEE Signal Processing Society
Feb 24, 2021
Recently, the coronavirus disease 2019 (COVID-19) has caused a pandemic disease in over 200 countries, influencing billions of humans. To control the infection, identifying and separating the infected people is the most crucial step. The main diagnos...
Correlation between lung infection severity and clinical laboratory indicators in patients with COVID-19: a cross-sectional study based on machine learning.
BMC infectious diseases
Feb 18, 2021
BACKGROUND: Coronavirus disease 2019 (COVID-19) has caused a global pandemic that has raised worldwide concern. This study aims to investigate the correlation between the extent of lung infection and relevant clinical laboratory testing indicators in...
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