AIMC Topic: Sample Size

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Sample-Size Determination Methodologies for Machine Learning in Medical Imaging Research: A Systematic Review.

Canadian Association of Radiologists journal = Journal l'Association canadienne des radiologistes
PURPOSE: The required training sample size for a particular machine learning (ML) model applied to medical imaging data is often unknown. The purpose of this study was to provide a descriptive review of current sample-size determination methodologies...

Using Deep Neural Networks to Reconstruct Non-uniformly Sampled NMR Spectra.

Journal of biomolecular NMR
Non-uniform and sparse sampling of multi-dimensional NMR spectra has over the last decade become an important tool to allow for fast acquisition of multi-dimensional NMR spectra with high resolution. The success of non-uniform sampling NMR hinge on b...

Linguistic summarization of in-home sensor data.

Journal of biomedical informatics
INTRODUCTION: With the increase in the population of older adults around the world, a significant amount of work has been done on in-home sensor technology to aid the elderly age independently. However, due to the large amounts of data generated by t...

Estimating the Population Average Treatment Effect in Observational Studies with Choice-Based Sampling.

The international journal of biostatistics
We consider causal inference in observational studies with choice-based sampling, in which subject enrollment is stratified on treatment choice. Choice-based sampling has been considered mainly in the econometrics literature, but it can be useful for...

Application of deep convolutional neural networks in classification of protein subcellular localization with microscopy images.

Genetic epidemiology
Single-cell microscopy image analysis has proved invaluable in protein subcellular localization for inferring gene/protein function. Fluorescent-tagged proteins across cellular compartments are tracked and imaged in response to genetic or environment...

Direct Feature Evaluation in Black-Box Optimization Using Problem Transformations.

Evolutionary computation
Exploratory Landscape Analysis provides sample-based methods to calculate features of black-box optimization problems in a quantitative and measurable way. Many problem features have been proposed in the literature in an attempt to provide insights i...

A Robust AUC Maximization Framework With Simultaneous Outlier Detection and Feature Selection for Positive-Unlabeled Classification.

IEEE transactions on neural networks and learning systems
The positive-unlabeled (PU) classification is a common scenario in real-world applications such as healthcare, text classification, and bioinformatics, in which we only observe a few samples labeled as "positive" together with a large volume of "unla...

The effect of machine learning regression algorithms and sample size on individualized behavioral prediction with functional connectivity features.

NeuroImage
Individualized behavioral/cognitive prediction using machine learning (ML) regression approaches is becoming increasingly applied. The specific ML regression algorithm and sample size are two key factors that non-trivially influence prediction accura...

Chronic obstructive lung disease "expert system": validation of a predictive tool for assisting diagnosis.

International journal of chronic obstructive pulmonary disease
PURPOSE: The purposes of this study were development and validation of an expert system (ES) aimed at supporting the diagnosis of chronic obstructive lung disease (COLD).