AIMC Topic: Lung Neoplasms

Clear Filters Showing 921 to 930 of 1629 articles

Novel gene signatures for stage classification of the squamous cell carcinoma of the lung.

Scientific reports Mar 1, 2021
The squamous cell carcinoma of the lung (SCLC) is one of the most common types of lung cancer. As GLOBOCAN reported in 2018, lung cancer was the first cause of death and new cases by cancer worldwide. Typically, diagnosis is made in the later stages ...
Machine Learning Female Biomarkers, Tumor Humans Gene Expression Regulation, Neoplastic Carcinoma, Non-Small-Cell Lung Databases, Nucleic Acid Male Gene Expression Profiling Lung Neoplasms
View on PubMed DOI

Computer-Aided Diagnosis Research of a Lung Tumor Based on a Deep Convolutional Neural Network and Global Features.

BioMed research international Feb 28, 2021
Based on the better generalization ability and the feature learning ability of the deep convolutional neural network, it is very significant to use the DCNN on the computer-aided diagnosis of a lung tumor. Firstly, a deep convolutional neural network...
Humans Deep Learning Tomography, X-Ray Computed Lung Neoplasms Diagnosis, Computer-Assisted
View on PubMed DOI

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.
Multiple Pulmonary Nodules Adolescent ROC Curve Lung Neoplasms Young Adult Adult Male Humans Middle Aged Prospective Studies Machine Learning Female Risk Assessment Tomography, X-Ray Computed Aged Lung Clinical Decision-Making Aged, 80 and over
View on PubMed DOI

A comparison between deep learning convolutional neural networks and radiologists in the differentiation of benign and malignant thyroid nodules on CT images.

Endokrynologia Polska Feb 23, 2021
INTRODUCTION: We designed 5 convolutional neural network (CNN) models and ensemble models to differentiate malignant and benign thyroid nodules on CT, and compared the diagnostic performance of CNN models with that of radiologists.
Retrospective Studies Tomography, X-Ray Computed Deep Learning Humans Neural Networks, Computer Thyroid Neoplasms Thyroid Nodule Lung Neoplasms Radiologists
View on PubMed DOI

An annotation-free whole-slide training approach to pathological classification of lung cancer types using deep learning.

Nature communications Feb 19, 2021
Deep learning for digital pathology is hindered by the extremely high spatial resolution of whole-slide images (WSIs). Most studies have employed patch-based methods, which often require detailed annotation of image patches. This typically involves l...
Adenocarcinoma Algorithms Carcinoma, Squamous Cell Deep Learning ROC Curve Image Processing, Computer-Assisted Lung Neoplasms Humans Neural Networks, Computer
View on PubMed DOI

Medical Image Classification Algorithm Based on Visual Attention Mechanism-MCNN.

Oxidative medicine and cellular longevity Feb 19, 2021
Due to the complexity of medical images, traditional medical image classification methods have been unable to meet the actual application needs. In recent years, the rapid development of deep learning theory has provided a technical approach for solv...
Neural Networks, Computer Humans Attention Breast Neoplasms Algorithms Image Processing, Computer-Assisted Female Lung Neoplasms
View on PubMed DOI

Predicting benign, preinvasive, and invasive lung nodules on computed tomography scans using machine learning.

The Journal of thoracic and cardiovascular surgery Feb 16, 2021
OBJECTIVE: The study objective was to investigate if machine learning algorithms can predict whether a lung nodule is benign, adenocarcinoma, or its preinvasive subtype from computed tomography images alone.
Lung Neoplasms Adenocarcinoma Adenoma Diagnosis, Differential Female Male Diagnosis, Computer-Assisted Algorithms Retrospective Studies Tomography, X-Ray Computed Humans Machine Learning
View on PubMed DOI

Deep Learning-based Super-Resolution Algorithm: Potential in the Management of Subsolid Nodules.

Radiology Feb 9, 2021
Deep Learning Lung Neoplasms Solitary Pulmonary Nodule Humans Algorithms
View on PubMed DOI

Computer-aided Detection of Subsolid Nodules at Chest CT: Improved Performance with Deep Learning-based CT Section Thickness Reduction.

Radiology Feb 9, 2021
Background Studies on the optimal CT section thickness for detecting subsolid nodules (SSNs) with computer-aided detection (CAD) are lacking. Purpose To assess the effect of CT section thickness on CAD performance in the detection of SSNs and to inve...
Female Aged Male Humans Retrospective Studies Tomography, X-Ray Computed Lung Neoplasms Middle Aged Multiple Pulmonary Nodules Diagnosis, Computer-Assisted Radiographic Image Interpretation, Computer-Assisted Deep Learning
View on PubMed DOI

Deep learning-based differentiation of invasive adenocarcinomas from preinvasive or minimally invasive lesions among pulmonary subsolid nodules.

European radiology Feb 8, 2021
OBJECTIVES: To evaluate a deep learning-based model using model-generated segmentation masks to differentiate invasive pulmonary adenocarcinoma (IPA) from preinvasive lesions or minimally invasive adenocarcinoma (MIA) on CT, making comparisons with r...
Humans Retrospective Studies Tomography, X-Ray Computed Deep Learning Neoplasm Invasiveness Diagnosis, Differential Lung Neoplasms Adenocarcinoma
View on PubMed DOI
Popular Topics
Recent Journals