AIMC Topic: ROC Curve
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A Transfer Learning Approach for Malignant Prostate Lesion Detection on Multiparametric MRI.
Technology in cancer research & treatment
Jan 1, 2019
PURPOSE: In prostate focal therapy, it is important to accurately localize malignant lesions in order to increase biological effect of the tumor region while achieving a reduction in dose to noncancerous tissue. In this work, we proposed a transfer l...
Breast mass detection and diagnosis using fused features with density.
Journal of X-ray science and technology
Jan 1, 2019
BACKGROUND: The morbidity of breast cancer has been increased in these years and ranked the first of all female diseases. Computer-aided diagnosis techniques for mammograms can help radiologists find early breast lesions. In mammograms, the degree of...
A computer-aided diagnosis system using artificial intelligence for the diagnosis and characterization of breast masses on ultrasound: Added value for the inexperienced breast radiologist.
Medicine
Jan 1, 2019
To evaluate the value of the computer-aided diagnosis (CAD) program applied to diagnostic breast ultrasonography (US) based on operator experience.US images of 100 breast masses from 91 women over 2 months (from May to June 2015) were collected and r...
A Multi-View Deep Learning Framework for EEG Seizure Detection.
IEEE journal of biomedical and health informatics
Jan 1, 2019
The recent advances in pervasive sensing technologies have enabled us to monitor and analyze the multi-channel electroencephalogram (EEG) signals of epilepsy patients to prevent serious outcomes caused by epileptic seizures. To avoid manual visual in...
Expert-Level Diagnosis of Nonpigmented Skin Cancer by Combined Convolutional Neural Networks.
JAMA dermatology
Jan 1, 2019
IMPORTANCE: Convolutional neural networks (CNNs) achieve expert-level accuracy in the diagnosis of pigmented melanocytic lesions. However, the most common types of skin cancer are nonpigmented and nonmelanocytic, and are more difficult to diagnose.
Automated and flexible identification of complex disease: building a model for systemic lupus erythematosus using noisy labeling.
Journal of the American Medical Informatics Association : JAMIA
Jan 1, 2019
UNLABELLED: Accurate and efficient identification of complex chronic conditions in the electronic health record (EHR) is an important but challenging task that has historically relied on tedious clinician review and oversimplification of the disease....
Deep Learning in Diagnosis of Maxillary Sinusitis Using Conventional Radiography.
Investigative radiology
Jan 1, 2019
OBJECTIVES: The aim of this study was to compare the diagnostic performance of a deep learning algorithm with that of radiologists in diagnosing maxillary sinusitis on Waters' view radiographs.
[An artificial neural network model for glioma grading using image information].
Zhong nan da xue xue bao. Yi xue ban = Journal of Central South University. Medical sciences
Dec 28, 2018
To explore the feasibility and efficacy of artificial neural network for differentiating high-grade glioma and low-grade glioma using image information.
Methods: A total of 130 glioma patients with confirmed pathological diagnosis were selected retr...
Prognostic Value of NT-proBNP in Stable Coronary Artery Disease in Chinese Patients after Percutaneous Coronary Intervention in the Drug-eluting Stent Era.
Biomedical and environmental sciences : BES
Dec 1, 2018
OBJECTIVE: The predictive value of N-terminal pro-brain natriuretic peptide (NT-proBNP) in patients with stable coronary artery disease (SCAD) in the drug-eluting stent era is not yet clear. We aimed to evaluate the prognostic value of NT-proBNP in S...
Predicting Pressure Injury in Critical Care Patients: A Machine-Learning Model.
American journal of critical care : an official publication, American Association of Critical-Care Nurses
Nov 1, 2018
BACKGROUND: Hospital-acquired pressure injuries are a serious problem among critical care patients. Some can be prevented by using measures such as specialty beds, which are not feasible for every patient because of costs. However, decisions about wh...
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