Feature selection in image analysis: a survey

Bolón-Canedo, Verónica; Remeseiro, Beatriz

doi:10.1007/s10462-019-09750-3

Cited by 113 publications

(45 citation statements)

References 107 publications

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“…We anticipate that such situations are quite common, and impede the discovery of many new embedded patterns in regional cortical folds at various brain regions. In our future work, we hope to test this issue further, and computationally extend this framework by including feature subset selection procedures (Bolón-Canedo & Remeseiro, 2020; Chandrashekar & Sahin, 2014; Hua et al, 2005; Miao & Niu, 2016; Roy, Schaffer, & Laramee, 2015; Way, Sahiner, Hadjiiski, & Chan, 2010; Xue, Zhang, Browne, & Yao, 2016), so that investigators will have added guidance in order to narrow down on the set of physical attributes of a cortical fold that best explain the set of embedded shapes.…”

Section: Discussionmentioning

confidence: 99%

A pipeline to characterize local cortical folds by mapping them to human-interpretable shapes

McMillen¹,

Beiler²,

Snyder³

et al. 2020

Preprint

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BackgroundVariations in regional cortical folds across individuals have been examined using computationally-derived morphological measures, or by manual characterization procedures that map distinct variants of a regional fold to a set of human-interpretable shapes. Although manual mapping approaches have proven useful for identifying morphological differences of clinical relevance, such procedures are subjective and not amenable to scaling.New MethodWe propose a 3-step pipeline to develop computational models of manual mapping. The steps are: represent regional folds as feature vectors, manually map each feature vector to a shape-variant that the underlying fold represents, and train classifiers to learn the mapping.ResultsFor demonstration, we chose a 2D-problem of detecting within slice discontinuity of medial and lateral sulci of orbitofrontal cortex (OFC); the discontinuity may be visualized as a broken H-shaped pattern, and is fundamental to OFC-type-characterization. The classifiers predicted discontinuities with 86-95% test-accuracy.Comparison with Existing MethodsThere is no existing pipeline that automates a manual characterization process. For the current demonstration problem, we conduct multiple analyses using existing softwares to explain our design decisions, and present guidelines for using the pipeline to examine other regional folds using conventional or non-conventional morphometric measures.ConclusionWe show that this pipeline can be useful for determining axial-slice discontinuity of sulci in the OFC and can learn structural-features that human-raters may rely on during manual-characterization.The pipeline can be used for examining other regional folds and may facilitate discovery of various statistically-reliable 2D or 3D human-interpretable shapes that are embedded throughout the brain.

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Section: Discussionmentioning

confidence: 99%

A pipeline to characterize local cortical folds by mapping them to human-interpretable shapes

McMillen¹,

Beiler²,

Snyder³

et al. 2020

Preprint

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show abstract

“…On the other hand there is an extensive body of work that uses AI as a tool for the analysis of large and complex data-sets and these are less concerned with what this tells us about human cognition and decision-making. However these methods often make use of ideas from information theory in order to improve performance, for example in feature selection tasks mutual information is used to measure the relevance of different features [ 39 ] as well as feature selection using entropy-based filters [ 40 ], information gain methods [ 41 ], one-shot learning [ 42 ], and deep learning [ 43 ]. In these applications information theory plays an important role, but the emphasis is towards the practical implementation of effective algorithms for data analysis, rather than the foundations of decision-making.…”

Section: Introductionmentioning

confidence: 99%

Information Theory for Agents in Artificial Intelligence, Psychology, and Economics

Harré

2021

Entropy

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This review covers some of the central relationships between artificial intelligence, psychology, and economics through the lens of information theory, specifically focusing on formal models of decision-theory. In doing so we look at a particular approach that each field has adopted and how information theory has informed the development of the ideas of each field. A key theme is expected utility theory, its connection to information theory, and the Bayesian approach to decision-making and forms of (bounded) rationality. What emerges from this review is a broadly unified formal perspective derived from three very different starting points that reflect the unique principles of each field. Each of the three approaches reviewed can, in principle at least, be implemented in a computational model in such a way that, with sufficient computational power, they could be compared with human abilities in complex tasks. However, a central critique that can be applied to all three approaches was first put forward by Savage in The Foundations of Statistics and recently brought to the fore by the economist Binmore: Bayesian approaches to decision-making work in what Savage called `small worlds’ but cannot work in `large worlds’. This point, in various different guises, is central to some of the current debates about the power of artificial intelligence and its relationship to human-like learning and decision-making. Recent work on artificial intelligence has gone some way to bridging this gap but significant questions still need to be answered in all three fields in order to make progress on these problems.

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“…There are many methods of selecting features. The feature selection methods are broken down into three basic categories: filters, wrappers and embedded methods [4]. In recent years, researchers have developed many methods to select features through IT tools [6,11,32].…”

Section: Introductionmentioning

confidence: 99%

Przegląd Metod Selekcji Cech Używanych W Diagnostyce Czerniaka

Michalska

2021

IAPGOS

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Obecnie stosuje się wiele metod selekcji cech. Cieszą się coraz większym zainteresowaniem badaczy. Oczywiście niektóre metody są stosowane częściej. W artykule zostały opisane podstawy działania algorytmów opartych na selekcji. Metody selekcji cech należące dzielą się na trzy kategorie: metody filtrowe, metody opakowujące, metody wbudowane. Zwrócono szczególnie uwagę na znalezienie przykładów zastosowań opisanych metod w diagnostyce czerniaka skóry.

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Feature selection in image analysis: a survey

Cited by 113 publications

References 107 publications

A pipeline to characterize local cortical folds by mapping them to human-interpretable shapes

A pipeline to characterize local cortical folds by mapping them to human-interpretable shapes

Information Theory for Agents in Artificial Intelligence, Psychology, and Economics

Przegląd Metod Selekcji Cech Używanych W Diagnostyce Czerniaka

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