This study evaluates the performance of convolutional neural networks (CNNs) in risk stratifying the malignant potential of thyroid nodules alongside traditional methods such as American College of Radiology Thyroid Imaging Reporting and Data System ...
OBJECTIVE: Automatic diabetic retinopathy screening system based on neural networks has been used to detect diabetic retinopathy (DR). However, there is no quantitative synthesis of performance of these methods. We aimed to estimate the sensitivity a...
OBJECTIVE: Body composition comprises prognostic information in patients with various malignancies and can be opportunistically determined from routine computed tomography (CT) scans. However, accurate assessment of patients with alterations, for exa...
MOTIVATION: In evidence-based medicine, defining a clinical question in terms of the specific patient problem aids the physicians to efficiently identify appropriate resources and search for the best available evidence for medical treatment. In order...
Microscopy and microanalysis : the official journal of Microscopy Society of America, Microbeam Analysis Society, Microscopical Society of Canada
Jun 1, 2020
With the growing importance of three-dimensional and very large field of view imaging, acquisition time becomes a serious bottleneck. Additionally, dose reduction is of importance when imaging material like biological tissue that is sensitive to elec...
MOTIVATION: Identification of virulence factors (VFs) is critical to the elucidation of bacterial pathogenesis and prevention of related infectious diseases. Current computational methods for VF prediction focus on binary classification or involve on...
MOTIVATION: Protein domains are subunits that can fold and function independently. Correct domain boundary assignment is thus a critical step toward accurate protein structure and function analyses. There is, however, no efficient algorithm available...
SUMMARY: Although many quantitative structure-activity relationship (QSAR) models are trained and evaluated for their predictive merits, understanding what models have been learning is of critical importance. However, the interpretation and visualiza...
MOTIVATION: A unique challenge in predictive model building for omics data has been the small number of samples (n) versus the large amount of features (p). This 'n≪p' property brings difficulties for disease outcome classification using deep learnin...
Join thousands of healthcare professionals staying informed about the latest AI breakthroughs in medicine. Get curated insights delivered to your inbox.