AIMC Topic: Lung Neoplasms

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An Automatic Detection System of Lung Nodule Based on Multigroup Patch-Based Deep Learning Network.

IEEE journal of biomedical and health informatics
High-efficiency lung nodule detection dramatically contributes to the risk assessment of lung cancer. It is a significant and challenging task to quickly locate the exact positions of lung nodules. Extensive work has been done by researchers around t...

Central focused convolutional neural networks: Developing a data-driven model for lung nodule segmentation.

Medical image analysis
Accurate lung nodule segmentation from computed tomography (CT) images is of great importance for image-driven lung cancer analysis. However, the heterogeneity of lung nodules and the presence of similar visual characteristics between nodules and the...

Robotic versus thoracoscopic thymectomy: The current evidence.

The international journal of medical robotics + computer assisted surgery : MRCAS
BACKGROUND: The purpose of this study was to analyze all relevant comparative studies comparing robot-assisted minimally invasive thymectomy (RATS) and video-assisted thoracic surgery thymectomy (VATS) in terms of surgical and short-term outcomes.

Feature fusion for lung nodule classification.

International journal of computer assisted radiology and surgery
PURPOSE: This article examines feature-based nodule description for the purpose of nodule classification in chest computed tomography scanning.

Fully automatic detection of lung nodules in CT images using a hybrid feature set.

Medical physics
PURPOSE: The aim of this study was to develop a novel technique for lung nodule detection using an optimized feature set. This feature set has been achieved after rigorous experimentation, which has helped in reducing the false positives significantl...

Deep monocular 3D reconstruction for assisted navigation in bronchoscopy.

International journal of computer assisted radiology and surgery
PURPOSE: In bronchoschopy, computer vision systems for navigation assistance are an attractive low-cost solution to guide the endoscopist to target peripheral lesions for biopsy and histological analysis. We propose a decoupled deep learning architec...

Pulmonary nodule classification with deep residual networks.

International journal of computer assisted radiology and surgery
UNLABELLED: PURPOSE  : Lung cancer has the highest death rate among all cancers in the USA. In this work we focus on improving the ability of computer-aided diagnosis (CAD) systems to predict the malignancy of nodules from cropped CT images of lung n...

Computed tomography (CT)-compatible remote center of motion needle steering robot: Fusing CT images and electromagnetic sensor data.

Medical engineering & physics
Lung cancer is the most common cause of cancer-related death, and early detection can reduce the mortality rate. Patients with lung nodules greater than 10 mm usually undergo a computed tomography (CT)-guided biopsy. However, aligning the needle with...

Feasibility and safety of robot-assisted thoracic surgery for lung lobectomy in patients with non-small cell lung cancer: a systematic review and meta-analysis.

World journal of surgical oncology
BACKGROUND: The aim of this study is to evaluate the feasibility and safety of robot-assisted thoracic surgery (RATS) lobectomy versus video-assisted thoracic surgery (VATS) for lobectomy in patients with non-small cell lung cancer (NSCLC).

Towards automatic pulmonary nodule management in lung cancer screening with deep learning.

Scientific reports
The introduction of lung cancer screening programs will produce an unprecedented amount of chest CT scans in the near future, which radiologists will have to read in order to decide on a patient follow-up strategy. According to the current guidelines...