Background: To investigate the influence of artificial intelligent (AI) based on deep learning on... more Background: To investigate the influence of artificial intelligent (AI) based on deep learning on the diagnostic performance and consistency of inexperienced cardiovascular radiologists.Methods: We enrolled 196 patents who had undergone both CCTA and invasive coronary angiography (ICA) within 6 months. Four readers with less cardiovascular experience (Reader 1 to Reader 4) and two cardiovascular radiologists (level II, Reader 5 and Reader 6) evaluated all images for ≥50% coronary artery stenosis, with ICA as the gold standard. Reader 3 and Reader 4 interpreted with aid from an AI system, and the other four readers interpreted without the AI system. The sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV) and accuracy (area under the receiver operating characteristic curve (AUC)) of six readers were calculated at the patient and vessel levels. Additionally, we evaluated interobserver consistency between Reader 1 and Reader 2, Reader 3 and Reader ...
Purpose. To evaluate the diagnostic performance of deep learning with a multichannel fusion three... more Purpose. To evaluate the diagnostic performance of deep learning with a multichannel fusion three-dimensional convolutional neural network (MCF-3DCNN) in the differentiation of the pathologic grades of hepatocellular carcinoma (HCC) based on dynamic contrast-enhanced magnetic resonance images (DCE-MR images). Methods and Materials. Fifty-one histologically proven HCCs from 42 consecutive patients from January 2015 to September 2017 were included in this retrospective study. Pathologic examinations revealed nine well-differentiated (WD), 35 moderately differentiated (MD), and seven poorly differentiated (PD) HCCs. DCE-MR images with five phases were collected using a 3.0 Tesla MR scanner. The 4D-tensor representation was employed to organize the collected data in one temporal and three spatial dimensions by referring to the phases and 3D scanning slices of the DCE-MR images. A deep learning diagnosis model with MCF-3DCNN was proposed, and the structure of MCF-3DCNN was determined to ...
Journal of Information and Computational Science, 2006
The paper introduces a speech-driven visual speech synthesis system. The loosing-coupled-mapping ... more The paper introduces a speech-driven visual speech synthesis system. The loosing-coupled-mapping scheme is proposed to establish the correspondence between the acoustic speech class and the visual speech class. Employing the data-driven method in ...
2010 International Conference on Multimedia Technology, 2010
Abstract The paper proposes a kind of visual speech feature for the speaking mouth images from th... more Abstract The paper proposes a kind of visual speech feature for the speaking mouth images from the video combining clues of the shape and local teeth texture. The geometric feature we proposed based on the computing the Euclidian distant between each the feature ...
Background: To investigate the influence of artificial intelligent (AI) based on deep learning on... more Background: To investigate the influence of artificial intelligent (AI) based on deep learning on the diagnostic performance and consistency of inexperienced cardiovascular radiologists.Methods: We enrolled 196 patents who had undergone both CCTA and invasive coronary angiography (ICA) within 6 months. Four readers with less cardiovascular experience (Reader 1 to Reader 4) and two cardiovascular radiologists (level II, Reader 5 and Reader 6) evaluated all images for ≥50% coronary artery stenosis, with ICA as the gold standard. Reader 3 and Reader 4 interpreted with aid from an AI system, and the other four readers interpreted without the AI system. The sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV) and accuracy (area under the receiver operating characteristic curve (AUC)) of six readers were calculated at the patient and vessel levels. Additionally, we evaluated interobserver consistency between Reader 1 and Reader 2, Reader 3 and Reader ...
Purpose. To evaluate the diagnostic performance of deep learning with a multichannel fusion three... more Purpose. To evaluate the diagnostic performance of deep learning with a multichannel fusion three-dimensional convolutional neural network (MCF-3DCNN) in the differentiation of the pathologic grades of hepatocellular carcinoma (HCC) based on dynamic contrast-enhanced magnetic resonance images (DCE-MR images). Methods and Materials. Fifty-one histologically proven HCCs from 42 consecutive patients from January 2015 to September 2017 were included in this retrospective study. Pathologic examinations revealed nine well-differentiated (WD), 35 moderately differentiated (MD), and seven poorly differentiated (PD) HCCs. DCE-MR images with five phases were collected using a 3.0 Tesla MR scanner. The 4D-tensor representation was employed to organize the collected data in one temporal and three spatial dimensions by referring to the phases and 3D scanning slices of the DCE-MR images. A deep learning diagnosis model with MCF-3DCNN was proposed, and the structure of MCF-3DCNN was determined to ...
Journal of Information and Computational Science, 2006
The paper introduces a speech-driven visual speech synthesis system. The loosing-coupled-mapping ... more The paper introduces a speech-driven visual speech synthesis system. The loosing-coupled-mapping scheme is proposed to establish the correspondence between the acoustic speech class and the visual speech class. Employing the data-driven method in ...
2010 International Conference on Multimedia Technology, 2010
Abstract The paper proposes a kind of visual speech feature for the speaking mouth images from th... more Abstract The paper proposes a kind of visual speech feature for the speaking mouth images from the video combining clues of the shape and local teeth texture. The geometric feature we proposed based on the computing the Euclidian distant between each the feature ...
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