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Melanoma recognition by a deep learning convolutional neural network-Performance in different melanoma subtypes and localisations.
European journal of cancer (Oxford, England : 1990)
Jan 20, 2020
BACKGROUND: Deep learning convolutional neural networks (CNNs) show great potential for melanoma diagnosis. Melanoma thickness at diagnosis among others depends on melanoma localisation and subtype (e.g. advanced thickness in acrolentiginous or nodul...
Liver tissue classification of en face images by fractal dimension-based support vector machine.
Journal of biophotonics
Jan 16, 2020
Full-field optical coherence tomography (FF-OCT) has been reported with its label-free subcellular imaging performance. To realize quantitive cancer detection, the support vector machine model of classifying normal and cancerous human liver tissue is...
Prediction of pathological complete response to neoadjuvant chemotherapy in breast cancer using a deep learning (DL) method.
Thoracic cancer
Jan 16, 2020
BACKGROUND: The aim of the study was to develop a deep learning (DL) algorithm to evaluate the pathological complete response (pCR) to neoadjuvant chemotherapy in breast cancer.
Classification of Interstitial Lung Abnormality Patterns with an Ensemble of Deep Convolutional Neural Networks.
Scientific reports
Jan 15, 2020
Subtle interstitial changes in the lung parenchyma of smokers, known as Interstitial Lung Abnormalities (ILA), have been associated with clinical outcomes, including mortality, even in the absence of Interstitial Lung Disease (ILD). Although several ...
Convolutional neural network-automated hepatobiliary phase adequacy evaluation may optimize examination time.
European journal of radiology
Jan 14, 2020
PURPOSE: To develop and evaluate the performance of a fully-automated convolutional neural network (CNN)-based algorithm to evaluate hepatobiliary phase (HBP) adequacy of gadoxetate disodium (EOB)-enhanced MRI. Secondarily, we explored the potential ...
Deep learning, computer-aided radiography reading for tuberculosis: a diagnostic accuracy study from a tertiary hospital in India.
Scientific reports
Jan 14, 2020
In general, chest radiographs (CXR) have high sensitivity and moderate specificity for active pulmonary tuberculosis (PTB) screening when interpreted by human readers. However, they are challenging to scale due to hardware costs and the dearth of pro...
Identification and transfer of spatial transcriptomics signatures for cancer diagnosis.
Breast cancer research : BCR
Jan 13, 2020
BACKGROUND: Distinguishing ductal carcinoma in situ (DCIS) from invasive ductal carcinoma (IDC) regions in clinical biopsies constitutes a diagnostic challenge. Spatial transcriptomics (ST) is an in situ capturing method, which allows quantification ...
A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment.
Journal of visualized experiments : JoVE
Jan 11, 2020
Mild cognitive impairment (MCI) is the first sign of dementia among elderly populations and its early detection is crucial in our aging societies. Common MCI tests are time-consuming such that indiscriminate massive screening would not be cost-effect...
Polygenic risk scores outperform machine learning methods in predicting coronary artery disease status.
Genetic epidemiology
Jan 10, 2020
Coronary artery disease (CAD) is the leading global cause of mortality and has substantial heritability with a polygenic architecture. Recent approaches of risk prediction were based on polygenic risk scores (PRS) not taking possible nonlinear effect...
Machine Learning Supports Long Noncoding RNAs as Expression Markers for Endometrial Carcinoma.
BioMed research international
Jan 10, 2020
Uterine corpus endometrial carcinoma (UCEC) is the second most common type of gynecological tumor. Several research studies have recently shown the potential of different ncRNAs as biomarkers for prognostics and diagnosis in different types of cancer...
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