AIMC Topic: Lung Neoplasms
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Automated detection of lung cancer at ultralow dose PET/CT by deep neural networks - Initial results.
Lung cancer (Amsterdam, Netherlands)
Nov 3, 2018
OBJECTIVES: We evaluated whether machine learning may be helpful for the detection of lung cancer in FDG-PET imaging in the setting of ultralow dose PET scans.
Convolutional Neural Networks Promising in Lung Cancer T-Parameter Assessment on Baseline FDG-PET/CT.
Contrast media & molecular imaging
Oct 30, 2018
AIM: To develop an algorithm, based on convolutional neural network (CNN), for the classification of lung cancer lesions as T1-T2 or T3-T4 on staging fluorodeoxyglucose positron emission tomography (FDG-PET)/CT images.
A collaborative computer aided diagnosis (C-CAD) system with eye-tracking, sparse attentional model, and deep learning.
Medical image analysis
Oct 28, 2018
Computer aided diagnosis (CAD) tools help radiologists to reduce diagnostic errors such as missing tumors and misdiagnosis. Vision researchers have been analyzing behaviors of radiologists during screening to understand how and why they miss tumors o...
Knowledge-based Collaborative Deep Learning for Benign-Malignant Lung Nodule Classification on Chest CT.
IEEE transactions on medical imaging
Oct 17, 2018
The accurate identification of malignant lung nodules on chest CT is critical for the early detection of lung cancer, which also offers patients the best chance of cure. Deep learning methods have recently been successfully introduced to computer vis...
Automatic lung nodule detection using a 3D deep convolutional neural network combined with a multi-scale prediction strategy in chest CTs.
Computers in biology and medicine
Oct 12, 2018
OBJECTIVE: A novel computer-aided detection (CAD) scheme for lung nodule detection using a 3D deep convolutional neural network combined with a multi-scale prediction strategy is proposed to assist radiologists by providing a second opinion on accura...
3-D Quantification of Filopodia in Motile Cancer Cells.
IEEE transactions on medical imaging
Oct 5, 2018
We present a 3D bioimage analysis workflow to quantitatively analyze single, actin-stained cells with filopodial protrusions of diverse structural and temporal attributes, such as number, length, thickness, level of branching, and lifetime, in time-l...
A semi-supervised deep learning method based on stacked sparse auto-encoder for cancer prediction using RNA-seq data.
Computer methods and programs in biomedicine
Oct 5, 2018
BACKGROUND AND OBJECTIVE: Cancer has become a complex health problem due to its high mortality. Over the past few decades, with the rapid development of the high-throughput sequencing technology and the application of various machine learning methods...
Diagnosis, Computer-Assisted
Deep Learning
Lung Neoplasms
Sequence Analysis, RNA
RNA
Pattern Recognition, Automated
False Positive Reactions
Gene Expression Regulation, Neoplastic
Supervised Machine Learning
Reproducibility of Results
Stomach Neoplasms
Breast Neoplasms
Male
Humans
Software
Algorithms
Female
Effect of machine learning methods on predicting NSCLC overall survival time based on Radiomics analysis.
Radiation oncology (London, England)
Oct 5, 2018
BACKGROUND: To investigate the effect of machine learning methods on predicting the Overall Survival (OS) for non-small cell lung cancer based on radiomics features analysis.
3D Deep Learning from CT Scans Predicts Tumor Invasiveness of Subcentimeter Pulmonary Adenocarcinomas.
Cancer research
Oct 2, 2018
: Identification of early-stage pulmonary adenocarcinomas before surgery, especially in cases of subcentimeter cancers, would be clinically important and could provide guidance to clinical decision making. In this study, we developed a deep learning ...
Adenocarcinoma
Neoplasm Invasiveness
Decision Making
Area Under Curve
Adolescent
Imaging, Three-Dimensional
Image Processing, Computer-Assisted
Lung Neoplasms
Precision Medicine
Prognosis
Mutation
Retrospective Studies
Female
Humans
Adult
Middle Aged
Tomography, X-Ray Computed
Aged
Neural Networks, Computer
Male
Diagnosis, Computer-Assisted
Aged, 80 and over
Deep Learning
Young Adult
Development and Validation of Deep Learning-based Automatic Detection Algorithm for Malignant Pulmonary Nodules on Chest Radiographs.
Radiology
Sep 25, 2018
Purpose To develop and validate a deep learning-based automatic detection algorithm (DLAD) for malignant pulmonary nodules on chest radiographs and to compare its performance with physicians including thoracic radiologists. Materials and Methods For ...
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