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Related Concept Videos

Randomized Experiments01:13

Randomized Experiments

The randomization process involves assigning study participants randomly to experimental or control groups based on their probability of being equally assigned. Randomization is meant to eliminate selection bias and balance known and unknown confounding factors so that the control group is similar to the treatment group as much as possible. A computer program and a random number generator can be used to assign participants to groups in a way that minimizes bias.
Simple randomization
Simple...
Random Sampling Method01:09

Random Sampling Method

Sampling is a technique to select a portion (or subset) of the larger population and study that portion (the sample) to gain information about the population. Data are the result of sampling from a population. The sampling method ensures that samples are drawn without bias and accurately represent the population. Because measuring the entire population in a study is not practical, researchers use samples to represent the population of interest. Among the various sampling methods used by...
Survival Tree01:19

Survival Tree

Survival trees are a non-parametric method used in survival analysis to model the relationship between a set of covariates and the time until an event of interest occurs, often referred to as the "time-to-event" or "survival time." This method is particularly useful when dealing with censored data, where the event has not occurred for some individuals by the end of the study period, or when the exact time of the event is unknown.
 Building a Survival Tree
Constructing a survival tree begins...
Random Variables01:09

Random Variables

A random variable is a single numerical value that indicates the outcome of a procedure. The concept of random variables is fundamental to the probability theory and was introduced by a Russian mathematician, Pafnuty Chebyshev, in the mid-nineteenth century.
Uppercase letters such as X or Y denote a random variable. Lowercase letters like x or y denote the value of a random variable. If X is a random variable, then X is written in words, and x is given as a number.
For example, let X = the...
Cluster Sampling Method01:20

Cluster Sampling Method

Appropriate sampling methods ensure that samples are drawn without bias and accurately represent the population. Because measuring the entire population in a study is not practical, researchers use samples to represent the population of interest.
To choose a cluster sample, divide the population into clusters (groups) and then randomly select some of the clusters. All the members from these clusters are in the cluster sample. For example, if you randomly sample four departments from your...
Application of Linearization and Approximation01:29

Application of Linearization and Approximation

A drone flying through complex terrain often relies on more than one sensing method to estimate small changes in altitude. Along with direct measurements, air pressure provides a useful indirect indicator of vertical movement. Atmospheric pressure decreases as altitude increases, and this relationship is commonly described using an exponential model. Although accurate, converting pressure measurements into altitude values requires calculations that are too complex to perform repeatedly during...

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Related Experiment Video

Updated: May 10, 2026

Development of an Individual-Tree Basal Area Increment Model using a Linear Mixed-Effects Approach
04:35

Development of an Individual-Tree Basal Area Increment Model using a Linear Mixed-Effects Approach

Published on: July 3, 2020

Neighbourhood approximation using randomized forests.

Ender Konukoglu1, Ben Glocker, Darko Zikic

  • 1Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital/Harvard Medical School, MA 02129, USA. ender.konukoglu@live.com

Medical Image Analysis
|June 4, 2013
PubMed
Summary
This summary is machine-generated.

Neighbourhood Approximation Forests (NAFs) efficiently find similar images for medical analysis. This method overcomes challenges in identifying neighbours for new images, improving accuracy and computational speed in applications like brain MRI and CT scans.

Keywords:
Approximate nearest neighboursImage-based regressionPatch-based segmentationRandom decision forestsSupervised neighbour search

Related Experiment Videos

Last Updated: May 10, 2026

Development of an Individual-Tree Basal Area Increment Model using a Linear Mixed-Effects Approach
04:35

Development of an Individual-Tree Basal Area Increment Model using a Linear Mixed-Effects Approach

Published on: July 3, 2020

Area of Science:

  • Medical Image Analysis
  • Machine Learning
  • Computer Vision

Background:

  • Leveraging annotated data is crucial for modern medical image analysis.
  • Image neighbourhood analysis shows potential but faces challenges in neighbour identification for new images.
  • Current neighbour identification methods are computationally expensive or infeasible for semantic distances.

Purpose of the Study:

  • Introduce Neighbourhood Approximation Forests (NAFs) for efficient approximate nearest neighbour retrieval.
  • Develop a general and efficient supervised learning algorithm for arbitrary distance metrics.
  • Enable efficient neighbour inference for out-of-sample images, even with semantic distance definitions.

Main Methods:

  • NAFs utilize appearance-based features to cluster images, approximating the neighbourhood structure induced by a user-defined distance.
  • The algorithm learns to infer nearest neighbours for unseen images.
  • Experimental evaluation was conducted on brain MRI age prediction and CT image segmentation.

Main Results:

  • NAFs demonstrate efficient inference of nearest neighbours for out-of-sample images.
  • The method proves effective even when the distance metric is based on semantic information.
  • Experimental results highlight the performance and computational benefits of NAFs.

Conclusions:

  • NAFs offer a general and efficient solution for approximate nearest neighbour retrieval in medical image analysis.
  • The algorithm successfully addresses the limitations of existing neighbour identification techniques.
  • NAFs show significant potential for diverse image analysis applications, including age prediction and segmentation.