Radiomics deals with the statistical analysis of radiologic image data. In this article, radiomics is introduced and some of its applications are presented. In particular, an example is used to demonstrate that pathology and radiology can work togeth...
Aortic dissections and ruptures are life-threatening injuries that must be immediately treated. Our national radiology practice receives dozens of these cases each month, but no automated process is currently available to check for critical pathologi...
A robust lung segmentation method using a deep convolutional neural network (CNN) was developed and evaluated on high-resolution computed tomography (HRCT) and volumetric CT of various types of diffuse interstitial lung disease (DILD). Chest CT image...
Low-dose computed tomography (CT) lung cancer screening is recommended by the US Preventive Services Task Force for high lung cancer-risk populations. In this study, we investigated an important factor affecting the CT dose-the scan length, for this ...
Remarkable progress has been made in image classification and segmentation, due to the recent study of deep convolutional neural networks (CNNs). To solve the similar problem of diagnostic lung nodule detection in low-dose computed tomography (CT) sc...
Computer-aided diagnosis (CAD) has already been widely used in medical image processing. We recently make another trial to implement convolutional neural network (CNN) on the classification of pulmonary nodules of thoracic CT images. The biggest chal...
Ensuring correct radiograph view labeling is important for machine learning algorithm development and quality control of studies obtained from multiple facilities. The purpose of this study was to develop and test the performance of a deep convolutio...
PURPOSE: The purpose of this study was to evaluate the impact of artificial intelligence (AI)-based noise reduction algorithm on aorta computed tomography angiography (CTA) image quality (IQ) at 80 kVp tube voltage and 40 mL contrast medium (CM).
Visual search behaviour and the interpretation of mammograms have been studied for errors in breast cancer detection. We aim to ascertain whether machine-learning models can learn about radiologists' attentional level and the interpretation of mammog...
Our objective is to evaluate the effectiveness of efficient convolutional neural networks (CNNs) for abnormality detection in chest radiographs and investigate the generalizability of our models on data from independent sources. We used the National ...