Journal of chemical information and modeling
Jul 19, 2019
Molecular dynamics simulations provide valuable insights into the behavior of molecular systems. Extending the recent trend of using machine learning techniques to predict physicochemical properties from molecular dynamics data, we propose to conside...
Histopathological images contain morphological markers of disease progression that have diagnostic and predictive values. In this study, we demonstrate how deep learning framework can be used for an automatic classification of Renal Cell Carcinoma (R...
BACKGROUND: Visualization tools for deep learning models typically focus on discovering key input features without considering how such low level features are combined in intermediate layers to make decisions. Moreover, many of these methods examine ...
PURPOSE: Nyquist ghost artifacts in echo planar imaging (EPI) are originated from phase mismatch between the even and odd echoes. However, conventional correction methods using reference scans often produce erroneous results especially in high-field ...
The Annals of otology, rhinology, and laryngology
Jul 18, 2019
OBJECTIVES: This article reviews the principles of unsupervised learning, a novel technique which has increasingly been reported as a tool for the investigation of chronic rhinosinusitis (CRS). It represents a paradigm shift from the traditional appr...
European journal of cancer (Oxford, England : 1990)
Jul 18, 2019
BACKGROUND: The diagnosis of most cancers is made by a board-certified pathologist based on a tissue biopsy under the microscope. Recent research reveals a high discordance between individual pathologists. For melanoma, the literature reports on 25-2...
Computational intelligence and neuroscience
Jul 18, 2019
Object retrieval plays an increasingly important role in video surveillance, digital marketing, e-commerce, etc. It is facing challenges such as large-scale datasets, imbalanced data, viewpoint, cluster background, and fine-grained details (attribute...
OBJECTIVES: This study sought to develop a fully automated framework for cardiac function analysis from cardiac magnetic resonance (CMR), including comprehensive quality control (QC) algorithms to detect erroneous output.
While convolutional neural network (CNN) has been demonstrating powerful ability to learn hierarchical spatial features from medical images, it is still difficult to apply it directly to resting-state functional MRI (rs-fMRI) and the derived brain fu...
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