Published on 01 October 2018
DistalPhalanxTW UCR Archive Dataset
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This dataset is part of the UCR Archive maintained by University of Southampton researchers. Please cite a relevant or the latest full archive release if you use the datasets. See http://www.timeseriesclassification.com/.This series of 11 classification problems were created as part of Luke Davis's PhD titled "Predictive Modelling of Bone Ageing". They are all derived from the same images, extracted from Cao et al. "Digital hand atlas and web-based bone age assessment: system design and implementation". They are designed to test the efficacy of hand and bone outline detection and whether these outlines could be helpful in bone age prediction. Algorithms to automatically extract the hand outlines and then the outlines of three bones of the middle finger (proximal, middle and distal phalanges) were applied to over 1300 images, and three human evaluators labelled the output of the image outlining as correct or incorrect. This generated three classification problems: DistalPhalanxOutlineCorrect; MiddlePhalanxOutlineCorrect; and ProximalPhalanxOutlineCorrect. The next stage of the project was to use the outlines to predict information about the subjects age. The three problems DistalPhalanxOutlineAgeGroup, MiddlePhalanxOutlineAgeGroup and ProximalPhalanxOutlineAgeGroup involve using the outline of one of the phalanges to predict whether the subject is one of three age groups: 0-6 years old, 7-12 years old and 13-19 years old. Note that these problems are aligned by subject, and hence can be treated as a multi dimensional TSC problem. Problem Phalanges contains the concatenation of all three problems. Bone age estimation is usually performed by an expert with an algorithm called Tanner-Whitehouse. This involves scoring each bone into one of seven categories based on the stage of development. The final three bone image classification problems, DistalPhalanxTW, MiddlePhalanxTW and ProximalPhalanxTW, involve predicting the Tanner-Whitehouse score (as labelled by a human expert) from the outline.http://www.ncbi.nlm.nih.gov/pubmed/10940607Donator: L. Davis, A. Bagnall
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Metrics Over Time
Publication Details
Subfield
Sociology and Political Science
Field
Social Sciences
Domain
Social Sciences
Confidence Score
40%
Source
Scholar Data Model