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A size-invariant convolutional network with dense connectivity applied to retinal vessel segmentation measured by a unique index.
Computer methods and programs in biomedicine
May 31, 2020
BACKGROUND AND OBJECTIVES: Retinal vessel segmentation (RVS) helps in diagnosing diseases such as hypertension, cardiovascular diseases, and others. Convolutional neural networks are widely used in RVS tasks. However, how to comprehensively evaluate ...
CAST: A multi-scale convolutional neural network based automated hippocampal subfield segmentation toolbox.
NeuroImage
May 29, 2020
In this study, we developed a multi-scale Convolutional neural network based Automated hippocampal subfield Segmentation Toolbox (CAST) for automated segmentation of hippocampal subfields. Although training CAST required approximately three days on a...
Modeling the Spread of COVID-19 Infection Using a Multilayer Perceptron.
Computational and mathematical methods in medicine
May 29, 2020
Coronavirus (COVID-19) is a highly infectious disease that has captured the attention of the worldwide public. Modeling of such diseases can be extremely important in the prediction of their impact. While classic, statistical, modeling can provide sa...
Application of explainable ensemble artificial intelligence model to categorization of hemodialysis-patient and treatment using nationwide-real-world data in Japan.
PloS one
May 29, 2020
BACKGROUND: Although dialysis patients are at a high risk of death, it is difficult for medical practitioners to simultaneously evaluate many inter-related risk factors. In this study, we evaluated the characteristics of hemodialysis patients using m...
Cluster Analysis
Kidney Failure, Chronic
Japan
Decision Making, Computer-Assisted
Cohort Studies
Prospective Studies
Humans
Logistic Models
Prognosis
Support Vector Machine
Male
Artificial Intelligence
Female
Aged
Middle Aged
Machine Learning
Risk Factors
Aged, 80 and over
Deep Learning
Databases, Factual
Adult
Renal Dialysis
Predicting host taxonomic information from viral genomes: A comparison of feature representations.
PLoS computational biology
May 26, 2020
The rise in metagenomics has led to an exponential growth in virus discovery. However, the majority of these new virus sequences have no assigned host. Current machine learning approaches to predicting virus host interactions have a tendency to focus...
The effects of different levels of realism on the training of CNNs with only synthetic images for the semantic segmentation of robotic instruments in a head phantom.
International journal of computer assisted radiology and surgery
May 22, 2020
PURPOSE: The manual generation of training data for the semantic segmentation of medical images using deep neural networks is a time-consuming and error-prone task. In this paper, we investigate the effect of different levels of realism on the traini...
Regularized least squares locality preserving projections with applications to image recognition.
Neural networks : the official journal of the International Neural Network Society
May 21, 2020
Locality preserving projection (LPP), as a well-known technique for dimensionality reduction, is designed to preserve the local structure of the original samples which usually lie on a low-dimensional manifold in the real world. However, it suffers f...
Performance of a convolutional neural network derived from an ECG database in recognizing myocardial infarction.
Scientific reports
May 21, 2020
Artificial intelligence (AI) is developing rapidly in the medical technology field, particularly in image analysis. ECG-diagnosis is an image analysis in the sense that cardiologists assess the waveforms presented in a 2-dimensional image. We hypothe...
Improved metabolomic data-based prediction of depressive symptoms using nonlinear machine learning with feature selection.
Translational psychiatry
May 19, 2020
To solve major limitations in algorithms for the metabolite-based prediction of psychiatric phenotypes, a novel prediction model for depressive symptoms based on nonlinear feature selection machine learning, the Hilbert-Schmidt independence criterion...
Predicting intra-operative and postoperative consequential events using machine-learning techniques in patients undergoing robot-assisted partial nephrectomy: a Vattikuti Collective Quality Initiative database study.
BJU international
May 18, 2020
OBJECTIVE: To predict intra-operative (IOEs) and postoperative events (POEs) consequential to the derailment of the ideal clinical course of patient recovery.
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