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Objective: False positive reduction is one of the most crucial components in an automated pulmonary nodule detection system, which plays an important role in lung cancer diagnosis and early treatment. The objective of this paper is to effectively address the challenges in this task and therefore to accurately discriminate the true nodules from a large number of candidates. Methods: We propose a novel...
Image-based precision medicine techniques can be used to better treat cancer patients. However, the gigapixel resolution of Whole Slide Histopathological Images (WSIs) makes traditional survival models computationally impossible. These models usually adopt manually labeled discriminative patches from region of interests (ROIs) and are unable to directly learn discriminative patches from WSIs. We argue...
The proposed system aims to detect the lung nodules from a series of CT scan images. Otsu's thresholding and morphological operations are applied for nodules segmentation. After segmentation, the objects that do not hold the possibility to be nodules are removed. Geometric, histogram as well as texture features are then extracted for benign and malignant nodules classification. Multilayer Perceptron...
The features of GGO nodules need to be obtained such as volume, mean, variance of Ground-Glass Opacity Nodules by boundaries of GGO nodules to judge malignant or benign of lung tumors. However, radiologists need to look for the slices including the GGO nodule in CT volume data. It is time-consuming. This paper proposes a semi-supervised learning method based on the label propagation. First, a GGO...
Lung cancer is the most common cancer for death among all cancers and CT scan is the best modality for imaging lung cancer. A good amount of research work has been carried out in the past towards CAD system for lung cancer detection using CT images. It is divided into four stages. They are pre-processing or lung segmentation, nodule detection, nodule segmentation and classification. This paper presents...
Quantitative imaging biomarkers identification has become a powerful tool for predictive diagnosis given increasingly available clinical imaging data. In parallel, molecular profiles have been well documented in non-small cell lung cancers (NSCLCs). However, there has been limited studies on leveraging the two major sources for improving lung cancer computer-aided diagnosis. In this paper, we investigate...
Characterization of lung nodules as benign or malignant is one of the most important tasks in lung cancer diagnosis, staging and treatment planning. While the variation in the appearance of the nodules remains large, there is a need for a fast and robust computer aided system. In this work, we propose an end-to-end trainable multi-view deep Convolutional Neural Network (CNN) for nodule characterization...
Detection of brain metastases in patients with undiagnosed primary cancer is unusual but still an existing phenomenon. In these cases, identifying the cancer site of origin is non-feasible by visual examination of magnetic resonance (MR) images. Recently, radiomics has been proposed to analyze differences among classes of visually imperceptible imaging characteristics. In this study we analyzed 46...
The increased utilization of Computer Aided diagnosis (CAD) in clinical procedures has been very effective in discovering numerous abnormalities in human beings. CAD of lung nodules can be safely employed to validate the opinion of radiologists in discovering existence of nodules and assess the existence and severity of lung cancer. This paper provides a comprehensive review of the existing automated...
Non-small Cell Lung Cancer (NSCLC) is a leading death disease in many countries. Many studies are focusing on exact surgical approaches to treat the disease. The five-year overall survival rate for NSCLC patients is typically predicted by traditional regression models with small samples and data size. In this paper, we introduce machine learning tools with feature selection algorithms and random forests...
This electronic document is a “live” template and already defines the components of your paper [title, text, heads, etc.] in its style sheet This paper provide a broad review for most important algorithms used in the CAD application for lung tissue diagnostics and highlighted the performance of each distinctive algorithm. Moreover, ROC characteristics have been made for each selected algorithms (support...
Lung cancer is a conceivably deadly disease brought on predominantly by ecological factors that transform genes that encodes basic cell regularities proteins. This paper investigates the early detection of lung cancer using computer aided diagnosis system which helps to improve the life term of the patient. The Existing multimodal sparse representation based classification of lung cancer related abnormalities...
CT image based lung nodule detection is the most widely used and accepted method for detecting lung cancer. Most CT image based methods are based on supervised/unsupervised learning, which has a high number of false positives and needs a large amount pre-segmented training samples. This problem can be solved, if a set of optimally small number of training samples can be created, where each sample...
Pulmonary nodule is a common lung disease, which can be prone to misdiagnosis and missed diagnosis. With the extensive application of CT technology, doctor's diagnostic efficiency has been greatly improved. However, the amount of CT image data is relatively large. Radiologists have to take a lot of time to read these images, and easy to overlook some minor lesions. Computer aided detection technology...
Imaging-genetic data mapping is important for clinical outcome prediction like survival analysis. In this paper, we propose a supervised conditional Gaussian graphical model (SuperCGGM) to uncover survival associated mapping between pathological images and genetic data. The proposed method integrates heterogeneous modal data into the survival model by weighted projection within the data. To obtain...
Lung cancer is the deadliest type of cancer in the world, for both men and women. Hence early detection is the most promising way to improve the patient's chance for survival from lung cancer. The most common technique used to examine the lung cancer is Posterior and Anterior chest radiography and computerized tomography scans. PA chest radiography is the cost effective tool in diagnosing lung tumors...
Many reports show that lung adenocarcinoma (LA) is currently diagnosed at the advanced stages with a lower survival rate, and highly sensitive to the epidermal growth factor receptor (EGFR) gene mutation status. Therefore, great research has been made to implement lung cancer screening programs using computed tomography (CT) imaging modality for early detection of disease. This study aims to distinguish...
Lung cancer is the foremost cause of death in many regions of the world. Early detection betters the chances of survival. PA chest radiography is the most commonly used diagnosis tool for detecting lung tumor, because it is cost effective and requires less radiation dose. Radiologists fail to detect nodule from PA chest radio graphs, at early stage because of complex anatomical structure present in...
Lung cancer is caused by abnormal and uncontrolled growth of cells in the lungs and the mortality rate of lung cancer is the highest among all types of cancer. It can be identified and treated with the help of computed tomography (CT) images. For an automated classifier, identifying good features from an image is a key concern. Deep feature extraction using pre-trained convolutional neural networks...
Computed tomography (CT) is widely used during diagnosis and treatment of Non-Small Cell Lung Cancer (NSCLC). Current computer-aided diagnosis (CAD) models, designed for the classification of malignant and benign nodules, use image features, selected by feature selectors, for making a decision. In this paper, we investigate automated selection of different image features informed by different nodule...
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