AIMC Topic: Lung Neoplasms

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Predicting lung nodule malignancies by combining deep convolutional neural network and handcrafted features.

Physics in medicine and biology Sep 4, 2019
To predict lung nodule malignancy with a high sensitivity and specificity for low dose CT (LDCT) lung cancer screening, we propose a fusion algorithm that combines handcrafted features (HF) into the features learned at the output layer of a 3D deep c...
Humans Databases, Factual Tomography, X-Ray Computed Lung Neoplasms Early Detection of Cancer Signal-To-Noise Ratio Sensitivity and Specificity Deep Learning Image Processing, Computer-Assisted
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Weakly Supervised Deep Learning for Whole Slide Lung Cancer Image Analysis.

IEEE transactions on cybernetics Sep 2, 2019
Histopathology image analysis serves as the gold standard for cancer diagnosis. Efficient and precise diagnosis is quite critical for the subsequent therapeutic treatment of patients. So far, computer-aided diagnosis has not been widely applied in pa...
Supervised Machine Learning Humans Image Interpretation, Computer-Assisted Histocytochemistry Deep Learning Lung Neoplasms
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PET image denoising using unsupervised deep learning.

European journal of nuclear medicine and molecular imaging Aug 29, 2019
PURPOSE: Image quality of positron emission tomography (PET) is limited by various physical degradation factors. Our study aims to perform PET image denoising by utilizing prior information from the same patient. The proposed method is based on unsup...
Male Signal-To-Noise Ratio Aged Positron-Emission Tomography Middle Aged Aged, 80 and over Image Processing, Computer-Assisted Humans Phantoms, Imaging Deep Learning Adult Quality Control Lung Neoplasms Female Image Enhancement Unsupervised Machine Learning
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The effects of physics-based data augmentation on the generalizability of deep neural networks: Demonstration on nodule false-positive reduction.

Medical physics Aug 27, 2019
PURPOSE: An important challenge for deep learning models is generalizing to new datasets that may be acquired with acquisition protocols different from the training set. It is not always feasible to expand training data to the range encountered in cl...
Humans Tomography, X-Ray Computed Sensitivity and Specificity False Positive Reactions Radiation Dosage Normal Distribution Deep Learning Image Processing, Computer-Assisted Lung Neoplasms
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DeepOrganNet: On-the-Fly Reconstruction and Visualization of 3D / 4D Lung Models from Single-View Projections by Deep Deformation Network.

IEEE transactions on visualization and computer graphics Aug 22, 2019
This paper introduces a deep neural network based method, i.e., DeepOrganNet, to generate and visualize fully high-fidelity 3D / 4D organ geometric models from single-view medical images with complicated background in real time. Traditional 3D / 4D m...
Humans Algorithms Neural Networks, Computer Phantoms, Imaging Lung Deep Learning Cone-Beam Computed Tomography Imaging, Three-Dimensional Lung Neoplasms
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Cross-modality (CT-MRI) prior augmented deep learning for robust lung tumor segmentation from small MR datasets.

Medical physics Aug 20, 2019
PURPOSE: Accurate tumor segmentation is a requirement for magnetic resonance (MR)-based radiotherapy. Lack of large expert annotated MR datasets makes training deep learning models difficult. Therefore, a cross-modality (MR-CT) deep learning segmenta...
Carcinoma, Non-Small-Cell Lung Magnetic Resonance Imaging Multimodal Imaging Deep Learning Humans Image Processing, Computer-Assisted Tomography, X-Ray Computed Lung Neoplasms
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Automatic Pulmonary Nodule Detection in CT Scans Using Convolutional Neural Networks Based on Maximum Intensity Projection.

IEEE transactions on medical imaging Aug 15, 2019
Accurate pulmonary nodule detection is a crucial step in lung cancer screening. Computer-aided detection (CAD) systems are not routinely used by radiologists for pulmonary nodule detection in clinical practice despite their potential benefits. Maximu...
Early Detection of Cancer Radiographic Image Interpretation, Computer-Assisted Databases, Factual Humans Tomography, X-Ray Computed Imaging, Three-Dimensional Neural Networks, Computer Sensitivity and Specificity Lung Neoplasms Solitary Pulmonary Nodule
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Multi-Task Deep Model With Margin Ranking Loss for Lung Nodule Analysis.

IEEE transactions on medical imaging Aug 12, 2019
Lung cancer is the leading cause of cancer deaths worldwide and early diagnosis of lung nodule is of great importance for therapeutic treatment and saving lives. Automated lung nodule analysis requires both accurate lung nodule benign-malignant class...
Image Processing, Computer-Assisted Lung Neoplasms Regression Analysis Humans Databases, Factual Deep Learning
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Lung segmentation method with dilated convolution based on VGG-16 network.

Computer assisted surgery (Abingdon, England) Aug 12, 2019
Lung cancer has become one of the life-threatening killers. Lung disease need to be assisted by CT images taken doctor's diagnosis, and the segmented CT image of the lung parenchyma is the first step to help doctor diagnosis. For the problem of accur...
Sensitivity and Specificity Lung Neoplasms Radiographic Image Interpretation, Computer-Assisted Humans Neural Networks, Computer Tomography, X-Ray Computed
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An investigation of CNN models for differentiating malignant from benign lesions using small pathologically proven datasets.

Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society Aug 11, 2019
Cancer has been one of the most threatening diseases to human health. There have been many efforts devoted to the advancement of radiology and transformative tools (e.g. non-invasive computed tomographic or CT imaging) to detect cancer in early stage...
Datasets as Topic Humans Diagnosis, Computer-Assisted Lung Neoplasms Male Neural Networks, Computer Diagnosis, Differential Aged Middle Aged Tomography, X-Ray Computed Aged, 80 and over Female Colorectal Neoplasms
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