AIMC Topic:
Databases, Factual
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Classification and comparison via neural networks.
Neural networks : the official journal of the International Neural Network Society
Jun 19, 2019
We consider learning from comparison labels generated as follows: given two samples in a dataset, a labeler produces a label indicating their relative order. Such comparison labels scale quadratically with the dataset size; most importantly, in pract...
Domain transformation on biological event extraction by learning methods.
Journal of biomedical informatics
Jun 18, 2019
Event extraction and annotation has become a significant focus of recent efforts in biological text mining and information extraction (IE). However, event extraction, event annotation methods, and resources have so far focused almost exclusively on a...
Studying the Manifold Structure of Alzheimer's Disease: A Deep Learning Approach Using Convolutional Autoencoders.
IEEE journal of biomedical and health informatics
Jun 17, 2019
Many classical machine learning techniques have been used to explore Alzheimer's disease (AD), evolving from image decomposition techniques such as principal component analysis toward higher complexity, non-linear decomposition algorithms. With the a...
Denoising of MR images with Rician noise using a wider neural network and noise range division.
Magnetic resonance imaging
Jun 17, 2019
Magnetic resonance (MR) images denoising is important in medical image analysis. Denoising methods based on deep learning have shown great promise and outperform all of the other conventional methods. However, deep-learning methods are limited by the...
Multitask learning and benchmarking with clinical time series data.
Scientific data
Jun 17, 2019
Health care is one of the most exciting frontiers in data mining and machine learning. Successful adoption of electronic health records (EHRs) created an explosion in digital clinical data available for analysis, but progress in machine learning for ...
The Classifying Autoencoder: Gaining Insight into Amyloid Assembly of Peptides and Proteins.
The journal of physical chemistry. B
Jun 13, 2019
Despite the importance of amyloid formation in disease pathology, the understanding of the primary structure?activity relationship for amyloid-forming peptides remains elusive. Here we use a new neural-network based method of analysis: the classifyin...
Self-Paced Balance Learning for Clinical Skin Disease Recognition.
IEEE transactions on neural networks and learning systems
Jun 12, 2019
Class imbalance is a challenging problem in many classification tasks. It induces biased classification results for minority classes that contain less training samples than others. Most existing approaches aim to remedy the imbalanced number of insta...
Comparative outcomes of minimally invasive and robotic-assisted esophagectomy.
Surgical endoscopy
Jun 10, 2019
OBJECTIVE: Minimally invasive esophagectomy (MIE) has demonstrated superior outcomes compared to open approaches. The myriad of techniques has precluded the recommendation of a standard approach. The addition of robotics to esophageal resection has p...
Sleep stage classification using covariance features of multi-channel physiological signals on Riemannian manifolds.
Computer methods and programs in biomedicine
Jun 10, 2019
BACKGROUND AND OBJECTIVE: The recognition of many sleep related pathologies highly relies on an accurate classification of sleep stages. Clinically, sleep stages are usually labelled by sleep experts through visually inspecting the whole-night polyso...
Principal Component Analysis based on Nuclear norm Minimization.
Neural networks : the official journal of the International Neural Network Society
Jun 8, 2019
Principal component analysis (PCA) is a widely used tool for dimensionality reduction and feature extraction in the field of computer vision. Traditional PCA is sensitive to outliers which are common in empirical applications. Therefore, in recent ye...
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