Translational research : the journal of laboratory and clinical medicine
Jun 6, 2018
Implications of plasminogen activator inhibitor-1 (PAI-1) in colonic polyps remain elusive. A prospective study was conducted with 188 consecutive subjects who underwent colonoscopy at a tertiary referral center. Biochemical parameters, serum PAI-1 l...
BACKGROUND: ADR is a widely used colonoscopy quality indicator. Calculation of ADR is labor-intensive and cumbersome using current electronic medical databases. Natural language processing (NLP) is a method used to extract meaning from unstructured o...
BACKGROUND AND AIM: Computer-aided diagnosis (CAD) is becoming a next-generation tool for the diagnosis of human disease. CAD for colon polyps has been suggested as a particularly useful tool for trainee colonoscopists, as the use of a CAD system avo...
BACKGROUND: In general, academic but not community endoscopists have demonstrated adequate endoscopic differentiation accuracy to make the 'resect and discard' paradigm for diminutive colorectal polyps workable. Computer analysis of video could poten...
Colorectal cancer (CRC) a leading cause of death by cancer, and screening programs for its early identification are at the heart of the increasing survival rates. To motivate population participation, non-invasive, accurate, scalable and cost-effecti...
IEEE journal of biomedical and health informatics
Dec 7, 2016
Automated polyp detection in colonoscopy videos has been demonstrated to be a promising way for colorectal cancer prevention and diagnosis. Traditional manual screening is time consuming, operator dependent, and error prone; hence, automated detectio...
IEEE journal of biomedical and health informatics
Dec 5, 2016
Colorectal cancer (CRC) is a leading cause of cancer deaths worldwide. Although polypectomy at early stage reduces CRC incidence, 90% of the polyps are small and diminutive, where removal of them poses risks to patients that may outweigh the benefits...
Computational and mathematical methods in medicine
Oct 26, 2016
Recently, Deep Learning, especially through Convolutional Neural Networks (CNNs) has been widely used to enable the extraction of highly representative features. This is done among the network layers by filtering, selecting, and using these features ...
Training a deep convolutional neural network (CNN) from scratch is difficult because it requires a large amount of labeled training data and a great deal of expertise to ensure proper convergence. A promising alternative is to fine-tune a CNN that ha...
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