AI Medical Compendium Topic:
Lung

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Optimized Deformable Model-based Segmentation and Deep Learning for Lung Cancer Classification.

The journal of medical investigation : JMI
Lung cancer is one of the life taking disease and causes more deaths worldwide. Early detection and treatment is necessary to save life. It is very difficult for doctors to interpret and identify diseases using imaging modalities alone. Therefore com...

LCDAE: Data Augmented Ensemble Framework for Lung Cancer Classification.

Technology in cancer research & treatment
The only possible solution to increase the patients' fatality rate is lung cancer early-stage detection. Recently, deep learning techniques became the most promising methods in medical image analysis compared with other numerous computer-aided diagn...

A Deep Learning Approach to Identify Chest Computed Tomography Features for Prediction of SARS-CoV-2 Infection Outcomes.

Methods in molecular biology (Clifton, N.J.)
There is still an urgent need to develop effective treatments to help minimize the cases of severe COVID-19. A number of tools have now been developed and applied to address these issues, such as the use of non-contrast chest computed tomography (CT)...

Parameter tuning in machine learning based on radiomics biomarkers of lung cancer.

Journal of X-ray science and technology
BACKGROUND: Lung cancer is one of the most common cancers, and early diagnosis and intervention can improve cancer cure rate.

Inferring Cell-type-specific Genes of Lung Cancer Based on Deep Learning.

Current gene therapy
BACKGROUND: Lung cancer is cancer with the highest incidence in the world, and there is obvious heterogeneity within its tumor. The emergence of single-cell sequencing technology allows researchers to obtain cell-type-specific expression genes at the...

Deep Learning-Based Internal Target Volume (ITV) Prediction Using Cone-Beam CT Images in Lung Stereotactic Body Radiotherapy.

Technology in cancer research & treatment
This study aims to develop a deep learning (DL)-based (Mask R-CNN) method to predict the internal target volume (ITV) in cone beam computed tomography (CBCT) images for lung stereotactic body radiotherapy (SBRT) patients and to evaluate the predictio...

Lung Nodule Detectability of Artificial Intelligence-assisted CT Image Reading in Lung Cancer Screening.

Current medical imaging
BACKGROUND: Artificial Intelligence (AI)-based automatic lung nodule detection system improves the detection rate of nodules. It is important to evaluate the clinical value of the AI system by comparing AI-assisted nodule detection with actual radiol...

Predictive Value of Immune Cell Functional Assay for Non-Cytomegalovirus Infection in Lung Transplant Recipients: A Multicenter Prospective Observational Study.

Archivos de bronconeumologia
INTRODUCTION: Immune cell functional assay (ImmuKnow®) is a non-invasive method that measures the state of cellular immunity in immunosuppressed patients. We studied the prognostic value of the assay for predicting non-cytomegalovirus (CMV) infection...

Unsupervised Detection of Lung Nodules in Chest Radiography Using Generative Adversarial Networks.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
Lung nodules are commonly missed in chest radiographs. We propose and evaluate P-AnoGAN, an unsupervised anomaly detection approach for lung nodules in radiographs. P-AnoGAN modifies the fast anomaly detection generative adversarial network (f-AnoGAN...

Dual Skip Connections Minimize the False Positive Rate of Lung Nodule Detection in CT images.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
Pulmonary cancer is one of the most commonly diagnosed and fatal cancers and is often diagnosed by incidental findings on computed tomography. Automated pulmonary nodule detection is an essential part of computer-aided diagnosis, which is still facin...