AIMC Topic: Radiographic Image Interpretation, Computer-Assisted
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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...
Mammographic Breast Density Assessment Using Deep Learning: Clinical Implementation.
Radiology
Oct 16, 2018
Purpose To develop a deep learning (DL) algorithm to assess mammographic breast density. Materials and Methods In this retrospective study, a deep convolutional neural network was trained to assess Breast Imaging Reporting and Data System (BI-RADS) b...
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...
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 ...
Detection of masses in mammograms using a one-stage object detector based on a deep convolutional neural network.
PloS one
Sep 18, 2018
Several computer aided diagnosis (CAD) systems have been developed for mammography. They are widely used in certain countries such as the U.S. where mammography studies are conducted more frequently; however, they are not yet globally employed for cl...
A review of computer aided detection in mammography.
Clinical imaging
Sep 7, 2018
Breast screening with mammography is widely recognized as the most effective method of detecting early breast cancer and has consistently demonstrated a 20-40% decrease in mortality among screened women. Despite this, the sensitivity of mammography r...
Single-view 2D CNNs with fully automatic non-nodule categorization for false positive reduction in pulmonary nodule detection.
Computer methods and programs in biomedicine
Aug 31, 2018
BACKGROUND AND OBJECTIVE: In pulmonary nodule detection, the first stage, candidate detection, aims to detect suspicious pulmonary nodules. However, detected candidates include many false positives and thus in the following stage, false positive redu...
Computer-aided detection in chest radiography based on artificial intelligence: a survey.
Biomedical engineering online
Aug 22, 2018
As the most common examination tool in medical practice, chest radiography has important clinical value in the diagnosis of disease. Thus, the automatic detection of chest disease based on chest radiography has become one of the hot topics in medical...
Demystification of AI-driven medical image interpretation: past, present and future.
European radiology
Aug 13, 2018
The recent explosion of 'big data' has ushered in a new era of artificial intelligence (AI) algorithms in every sphere of technological activity, including medicine, and in particular radiology. However, the recent success of AI in certain flagship a...
FissureNet: A Deep Learning Approach For Pulmonary Fissure Detection in CT Images.
IEEE transactions on medical imaging
Aug 10, 2018
Pulmonary fissure detection in computed tomography (CT) is a critical component for automatic lobar segmentation. The majority of fissure detection methods use feature descriptors that are hand-crafted, low-level, and have local spatial extent. The d...
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