AIMC Topic: Lung Neoplasms

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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...
Lung Neoplasms Attention Female Breast Neoplasms Image Processing, Computer-Assisted Humans Neural Networks, Computer Algorithms
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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.
Adenoma Diagnosis, Differential Diagnosis, Computer-Assisted Humans Lung Neoplasms Machine Learning Adenocarcinoma Tomography, X-Ray Computed Algorithms Female Retrospective Studies Male
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Deep Learning-based Super-Resolution Algorithm: Potential in the Management of Subsolid Nodules.

Radiology Feb 9, 2021
Lung Neoplasms Solitary Pulmonary Nodule Algorithms Deep Learning Humans
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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...
Multiple Pulmonary Nodules Radiographic Image Interpretation, Computer-Assisted Aged Male Middle Aged Diagnosis, Computer-Assisted Deep Learning Lung Neoplasms Retrospective Studies Tomography, X-Ray Computed Female Humans
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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...
Retrospective Studies Tomography, X-Ray Computed Deep Learning Humans Diagnosis, Differential Lung Neoplasms Adenocarcinoma Neoplasm Invasiveness
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A Staging Auxiliary Diagnosis Model for Nonsmall Cell Lung Cancer Based on the Intelligent Medical System.

Computational and mathematical methods in medicine Feb 8, 2021
At present, human health is threatened by many diseases, and lung cancer is one of the most dangerous tumors that threaten human life. In most developing countries, due to the large population and lack of medical resources, it is difficult for doctor...
China Lung Neoplasms Computational Biology Biomarkers, Tumor Humans Carcinoma, Non-Small-Cell Lung Neural Networks, Computer Diagnosis, Computer-Assisted Neoplasm Staging Algorithms Deep Learning Decision Making, Computer-Assisted Electronic Health Records
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Lung Cancer and Granuloma Identification Using a Deep Learning Model to Extract 3-Dimensional Radiomics Features in CT Imaging.

Clinical lung cancer Feb 6, 2021
BACKGROUND: We aimed to evaluate a deep learning (DL) model combining perinodular and intranodular radiomics features and clinical features for preoperative differentiation of solitary granuloma nodules (GNs) from solid lung cancer nodules in patient...
Humans Databases, Factual Retrospective Studies Middle Aged Tomography, X-Ray Computed Deep Learning Granuloma Lung Neoplasms Young Adult Female Adult Aged Male Aged, 80 and over
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Deep Learning Methods for Lung Cancer Segmentation in Whole-Slide Histopathology Images-The ACDC@LungHP Challenge 2019.

IEEE journal of biomedical and health informatics Feb 5, 2021
Accurate segmentation of lung cancer in pathology slides is a critical step in improving patient care. We proposed the ACDC@LungHP (Automatic Cancer Detection and Classification in Whole-slide Lung Histopathology) challenge for evaluating different c...
Humans Diagnosis, Computer-Assisted Deep Learning Lung Neoplasms
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Use of a Commercially Available Deep Learning Algorithm to Measure the Solid Portions of Lung Cancer Manifesting as Subsolid Lesions at CT: Comparisons with Radiologists and Invasive Component Size at Pathologic Examination.

Radiology Feb 2, 2021
Background The solid portion size of lung cancer lesions manifesting as subsolid lesions is key in their management, but the automatic measurement of such lesions by means of a deep learning (DL) algorithm needs evaluation. Purpose To evaluate the pe...
Middle Aged Deep Learning Lung Neoplasms Tomography, X-Ray Computed Female Male Software Adenocarcinoma Retrospective Studies Radiologists Image Interpretation, Computer-Assisted Humans
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Integrating Multiomics Information in Deep Learning Architectures for Joint Actuarial Outcome Prediction in Non-Small Cell Lung Cancer Patients After Radiation Therapy.

International journal of radiation oncology, biology, physics Feb 1, 2021
PURPOSE: Novel actuarial deep learning neural network (ADNN) architectures are proposed for joint prediction of radiation therapy outcomes-radiation pneumonitis (RP) and local control (LC)-in stage III non-small cell lung cancer (NSCLC) patients. Unl...
Humans Cytokines Female Carcinoma, Non-Small-Cell Lung MicroRNAs Positron-Emission Tomography Radiation Pneumonitis Aged Male Middle Aged Deep Learning Lung Neoplasms Treatment Outcome
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