AIMC Topic: Lung Neoplasms

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Technical Note: 3D localization of lung tumors on cone beam CT projections via a convolutional recurrent neural network.

Medical physics
PURPOSE: To design a convolutional recurrent neural network (CRNN) that calculates three-dimensional (3D) positions of lung tumors from continuously acquired cone beam computed tomography (CBCT) projections, and facilitates the sorting and reconstruc...

The Emerging Role of Radiomics in COPD and Lung Cancer.

Respiration; international review of thoracic diseases
Medical imaging plays a key role in evaluating and monitoring lung diseases such as chronic obstructive pulmonary disease (COPD) and lung cancer. The application of artificial intelligence in medical imaging has transformed medical images into mineab...

A multi-scale framework with unsupervised joint training of convolutional neural networks for pulmonary deformable image registration.

Physics in medicine and biology
To achieve accurate and fast deformable image registration (DIR) for pulmonary CT, we proposed a Multi-scale DIR framework with unsupervised Joint training of Convolutional Neural Network (MJ-CNN). MJ-CNN contains three models at multi-scale levels f...

An effective approach for CT lung segmentation using mask region-based convolutional neural networks.

Artificial intelligence in medicine
Computer vision systems have numerous tools to assist in various medical fields, notably in image diagnosis. Computed tomography (CT) is the principal imaging method used to assist in the diagnosis of diseases such as bone fractures, lung cancer, hea...

A Two-Stage Convolutional Neural Networks for Lung Nodule Detection.

IEEE journal of biomedical and health informatics
Early detection of lung cancer is an effective way to improve the survival rate of patients. It is a critical step to have accurate detection of lung nodules in computed tomography (CT) images for the diagnosis of lung cancer. However, due to the het...

Test-retest reproducibility of a deep learning-based automatic detection algorithm for the chest radiograph.

European radiology
OBJECTIVES: To perform test-retest reproducibility analyses for deep learning-based automatic detection algorithm (DLAD) using two stationary chest radiographs (CRs) with short-term intervals, to analyze influential factors on test-retest variations,...

Distributed learning on 20 000+ lung cancer patients - The Personal Health Train.

Radiotherapy and oncology : journal of the European Society for Therapeutic Radiology and Oncology
BACKGROUND AND PURPOSE: Access to healthcare data is indispensable for scientific progress and innovation. Sharing healthcare data is time-consuming and notoriously difficult due to privacy and regulatory concerns. The Personal Health Train (PHT) pro...

AI-based computer-aided diagnosis (AI-CAD): the latest review to read first.

Radiological physics and technology
The third artificial intelligence (AI) boom is coming, and there is an inkling that the speed of its evolution is quickly increasing. In games like chess, shogi, and go, AI has already defeated human champions, and the fact that it is able to achieve...