AIMC Topic: Lung Neoplasms

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Shape and margin-aware lung nodule classification in low-dose CT images via soft activation mapping.

Medical image analysis Dec 12, 2019
A number of studies on lung nodule classification lack clinical/biological interpretations of the features extracted by convolutional neural network (CNN). The methods like class activation mapping (CAM) and gradient-based CAM (Grad-CAM) are tailored...
Solitary Pulmonary Nodule Radiographic Image Interpretation, Computer-Assisted Neural Networks, Computer Tomography, X-Ray Computed Humans Sensitivity and Specificity Lung Neoplasms
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Artificial intelligence applications for thoracic imaging.

European journal of radiology Dec 11, 2019
Artificial intelligence is a hot topic in medical imaging. The development of deep learning methods and in particular the use of convolutional neural networks (CNNs), have led to substantial performance gain over the classic machine learning techniqu...
Artificial Intelligence Machine Learning Humans Early Detection of Cancer Algorithms Radiologists Radiography, Thoracic Deep Learning Lung Neoplasms Thorax
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Artificial intelligence for detecting small FDG-positive lung nodules in digital PET/CT: impact of image reconstructions on diagnostic performance.

European radiology Dec 10, 2019
OBJECTIVES: To evaluate the diagnostic performance of a deep learning algorithm for automated detection of small F-FDG-avid pulmonary nodules in PET scans, and to assess whether novel block sequential regularized expectation maximization (BSREM) reco...
Multiple Pulmonary Nodules Solitary Pulmonary Nodule Positron Emission Tomography Computed Tomography Radiopharmaceuticals Fluorodeoxyglucose F18 Middle Aged Deep Learning Image Processing, Computer-Assisted Lung Neoplasms Adult Sensitivity and Specificity Male Humans Artificial Intelligence Algorithms Aged Aged, 80 and over Retrospective Studies Female
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A deep residual learning network for predicting lung adenocarcinoma manifesting as ground-glass nodule on CT images.

European radiology Dec 6, 2019
OBJECTIVE: To develop a deep learning-based artificial intelligence (AI) scheme for predicting the likelihood of the ground-glass nodule (GGN) detected on CT images being invasive adenocarcinoma (IA) and also compare the accuracy of this AI scheme wi...
Female Aged Aged, 80 and over Young Adult Artificial Intelligence Retrospective Studies Tomography, X-Ray Computed Disease Progression Deep Learning Adolescent ROC Curve Adult Male Middle Aged Humans Adenocarcinoma in Situ Neural Networks, Computer Neoplasm Invasiveness Image Processing, Computer-Assisted Radiologists Lung Neoplasms Solitary Pulmonary Nodule Feasibility Studies Adenocarcinoma of Lung
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Predictive models for patients with lung carcinomas to identify EGFR mutation status via an artificial neural network based on multiple clinical information.

Journal of cancer research and clinical oncology Dec 5, 2019
PURPOSE: Epidermal growth factor receptor (EGFR) mutation testing has several limitations. Therefore, we built predictive models to determine the EGFR mutation status of patients and guide therapeutic decision-making.
Decision Making, Computer-Assisted Lung Neoplasms Mutation ErbB Receptors Humans Neural Networks, Computer Male Middle Aged Female Aged
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Natural language processing for populating lung cancer clinical research data.

BMC medical informatics and decision making Dec 5, 2019
BACKGROUND: Lung cancer is the second most common cancer for men and women; the wide adoption of electronic health records (EHRs) offers a potential to accelerate cohort-related epidemiological studies using informatics approaches. Since manual extra...
Artificial Intelligence Narration Humans Electronic Health Records Research Natural Language Processing Neoplasm Grading Female Data Collection Lung Neoplasms
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Graph temporal ensembling based semi-supervised convolutional neural network with noisy labels for histopathology image analysis.

Medical image analysis Dec 2, 2019
Although convolutional neural networks have achieved tremendous success on histopathology image classification, they usually require large-scale clean annotated data and are sensitive to noisy labels. Unfortunately, labeling large-scale images is lab...
Male Image Processing, Computer-Assisted Lung Neoplasms Datasets as Topic Neural Networks, Computer Machine Learning Female Breast Neoplasms Humans
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Commentary: Maximum surgery through minimal incisions.

The Journal of thoracic and cardiovascular surgery Nov 30, 2019
Robotics Humans Robotic Surgical Procedures Lung Neoplasms Propensity Score Carcinoma, Non-Small-Cell Lung
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Evolving the pulmonary nodules diagnosis from classical approaches to deep learning-aided decision support: three decades' development course and future prospect.

Journal of cancer research and clinical oncology Nov 30, 2019
PURPOSE: Lung cancer is the commonest cause of cancer deaths worldwide, and its mortality can be reduced significantly by performing early diagnosis and screening. Since the 1960s, driven by the pressing needs to accurately and effectively interpret ...
Multiple Pulmonary Nodules Positron-Emission Tomography Humans Image Processing, Computer-Assisted Tomography, X-Ray Computed Lung Neoplasms Algorithms Deep Learning Decision Support Techniques
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Deep Learning for the Classification of Small (≤2 cm) Pulmonary Nodules on CT Imaging: A Preliminary Study.

Academic radiology Nov 25, 2019
RATIONALE AND OBJECTIVES: We aimed to present a deep learning-based malignancy prediction model (CT-lungNET) that is simpler and faster to use in the diagnosis of small (≤2 cm) pulmonary nodules on nonenhanced chest CT and to preliminarily evaluate i...
Tomography, X-Ray Computed Deep Learning Lung Neoplasms Humans Solitary Pulmonary Nodule Multiple Pulmonary Nodules Radiographic Image Interpretation, Computer-Assisted
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