AI Medical Compendium Topic:
Supervised Machine Learning

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A supervised machine learning approach to characterize spinal network function.

Journal of neurophysiology
Spontaneous activity is a common feature of immature neuronal networks throughout the central nervous system and plays an important role in network development and consolidation. In postnatal rodents, spontaneous activity in the spinal cord exhibits ...

Automated feature selection of predictors in electronic medical records data.

Biometrics
The use of Electronic Health Records (EHR) for translational research can be challenging due to difficulty in extracting accurate disease phenotype data. Historically, EHR algorithms for annotating phenotypes have been either rule-based or trained wi...

Snore-GANs: Improving Automatic Snore Sound Classification With Synthesized Data.

IEEE journal of biomedical and health informatics
One of the frontier issues that severely hamper the development of automatic snore sound classification (ASSC) associates to the lack of sufficient supervised training data. To cope with this problem, we propose a novel data augmentation approach bas...

Semi-supervised deep learning of brain tissue segmentation.

Neural networks : the official journal of the International Neural Network Society
Brain image segmentation is of great importance not only for clinical use but also for neuroscience research. Recent developments in deep neural networks (DNNs) have led to the application of DNNs to brain image segmentation, which required extensive...

An optimal time interval of input spikes involved in synaptic adjustment of spike sequence learning.

Neural networks : the official journal of the International Neural Network Society
The supervised learning methods for spiking neurons based on temporal encoding are important foundation for the development of spiking neural networks. During the learning process, the synaptic weights of a spiking neuron are adjusted to make the neu...

BTS-DSN: Deeply supervised neural network with short connections for retinal vessel segmentation.

International journal of medical informatics
BACKGROUND AND OBJECTIVE: The condition of vessel of the human eye is an important factor for the diagnosis of ophthalmological diseases. Vessel segmentation in fundus images is a challenging task due to complex vessel structure, the presence of simi...

Not-so-supervised: A survey of semi-supervised, multi-instance, and transfer learning in medical image analysis.

Medical image analysis
Machine learning (ML) algorithms have made a tremendous impact in the field of medical imaging. While medical imaging datasets have been growing in size, a challenge for supervised ML algorithms that is frequently mentioned is the lack of annotated d...

Learning mechanisms in cue reweighting.

Cognition
Feedback has been shown to be effective in shifting attention across perceptual cues to a phonological contrast in speech perception (Francis, Baldwin & Nusbaum, 2000). However, the learning mechanisms behind this process remain obscure. We compare t...

Weakly Supervised Deep Learning for Brain Disease Prognosis Using MRI and Incomplete Clinical Scores.

IEEE transactions on cybernetics
As a hot topic in brain disease prognosis, predicting clinical measures of subjects based on brain magnetic resonance imaging (MRI) data helps to assess the stage of pathology and predict future development of the disease. Due to incomplete clinical ...

A Novel Weakly Supervised Multitask Architecture for Retinal Lesions Segmentation on Fundus Images.

IEEE transactions on medical imaging
Obtaining the complete segmentation map of retinal lesions is the first step toward an automated diagnosis tool for retinopathy that is interpretable in its decision-making. However, the limited availability of ground truth lesion detection maps at a...