Automatic detection of referral patients due to retinal pathologies through data mining.
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| Title: | Automatic detection of referral patients due to retinal pathologies through data mining. |
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| Authors: | Quellec, Gwenolé1 gwenole.quellec@live.com, Lamard, Mathieu1,2, Erginay, Ali3, Chabouis, Agnès3, Massin, Pascale3, Cochener, Béatrice1,2,4, Cazuguel, Guy1,5 |
| Source: | Medical Image Analysis. Apr2016, Vol. 29, p47-64. 18p. |
| Subjects: | Retinal disease diagnosis, Data mining, Retinal diseases, Automation, Diabetic retinopathy, Prevention |
| Abstract: | With the increased prevalence of retinal pathologies, automating the detection of these pathologies is becoming more and more relevant. In the past few years, many algorithms have been developed for the automated detection of a specific pathology, typically diabetic retinopathy, using eye fundus photography. No matter how good these algorithms are, we believe many clinicians would not use automatic detection tools focusing on a single pathology and ignoring any other pathology present in the patient’s retinas. To solve this issue, an algorithm for characterizing the appearance of abnormal retinas, as well as the appearance of the normal ones, is presented. This algorithm does not focus on individual images: it considers examination records consisting of multiple photographs of each retina, together with contextual information about the patient. Specifically, it relies on data mining in order to learn diagnosis rules from characterizations of fundus examination records. The main novelty is that the content of examination records (images and context) is characterized at multiple levels of spatial and lexical granularity: 1) spatial flexibility is ensured by an adaptive decomposition of composite retinal images into a cascade of regions, 2) lexical granularity is ensured by an adaptive decomposition of the feature space into a cascade of visual words. This multigranular representation allows for great flexibility in automatically characterizing normality and abnormality: it is possible to generate diagnosis rules whose precision and generalization ability can be traded off depending on data availability. A variation on usual data mining algorithms, originally designed to mine static data, is proposed so that contextual and visual data at adaptive granularity levels can be mined. This framework was evaluated in e-ophtha , a dataset of 25,702 examination records from the OPHDIAT screening network, as well as in the publicly-available Messidor dataset. It was successfully applied to the detection of patients that should be referred to an ophthalmologist and also to the specific detection of several pathologies. [ABSTRACT FROM AUTHOR] |
| Copyright of Medical Image Analysis is the property of Elsevier B.V. and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.) | |
| Database: | Engineering Source |
| FullText | Text: Availability: 0 |
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| Header | DbId: egs DbLabel: Engineering Source An: 112677652 AccessLevel: 6 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 0 |
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| Items | – Name: Title Label: Title Group: Ti Data: Automatic detection of referral patients due to retinal pathologies through data mining. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Quellec%2C+Gwenolé%22">Quellec, Gwenolé</searchLink><relatesTo>1</relatesTo><i> gwenole.quellec@live.com</i><br /><searchLink fieldCode="AR" term="%22Lamard%2C+Mathieu%22">Lamard, Mathieu</searchLink><relatesTo>1,2</relatesTo><br /><searchLink fieldCode="AR" term="%22Erginay%2C+Ali%22">Erginay, Ali</searchLink><relatesTo>3</relatesTo><br /><searchLink fieldCode="AR" term="%22Chabouis%2C+Agnès%22">Chabouis, Agnès</searchLink><relatesTo>3</relatesTo><br /><searchLink fieldCode="AR" term="%22Massin%2C+Pascale%22">Massin, Pascale</searchLink><relatesTo>3</relatesTo><br /><searchLink fieldCode="AR" term="%22Cochener%2C+Béatrice%22">Cochener, Béatrice</searchLink><relatesTo>1,2,4</relatesTo><br /><searchLink fieldCode="AR" term="%22Cazuguel%2C+Guy%22">Cazuguel, Guy</searchLink><relatesTo>1,5</relatesTo> – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="JN" term="%22Medical+Image+Analysis%22">Medical Image Analysis</searchLink>. Apr2016, Vol. 29, p47-64. 18p. – Name: Subject Label: Subjects Group: Su Data: <searchLink fieldCode="DE" term="%22Retinal+disease+diagnosis%22">Retinal disease diagnosis</searchLink><br /><searchLink fieldCode="DE" term="%22Data+mining%22">Data mining</searchLink><br /><searchLink fieldCode="DE" term="%22Retinal+diseases%22">Retinal diseases</searchLink><br /><searchLink fieldCode="DE" term="%22Automation%22">Automation</searchLink><br /><searchLink fieldCode="DE" term="%22Diabetic+retinopathy%22">Diabetic retinopathy</searchLink><br /><searchLink fieldCode="DE" term="%22Prevention%22">Prevention</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: With the increased prevalence of retinal pathologies, automating the detection of these pathologies is becoming more and more relevant. In the past few years, many algorithms have been developed for the automated detection of a specific pathology, typically diabetic retinopathy, using eye fundus photography. No matter how good these algorithms are, we believe many clinicians would not use automatic detection tools focusing on a single pathology and ignoring any other pathology present in the patient’s retinas. To solve this issue, an algorithm for characterizing the appearance of abnormal retinas, as well as the appearance of the normal ones, is presented. This algorithm does not focus on individual images: it considers examination records consisting of multiple photographs of each retina, together with contextual information about the patient. Specifically, it relies on data mining in order to learn diagnosis rules from characterizations of fundus examination records. The main novelty is that the content of examination records (images and context) is characterized at multiple levels of spatial and lexical granularity: 1) spatial flexibility is ensured by an adaptive decomposition of composite retinal images into a cascade of regions, 2) lexical granularity is ensured by an adaptive decomposition of the feature space into a cascade of visual words. This multigranular representation allows for great flexibility in automatically characterizing normality and abnormality: it is possible to generate diagnosis rules whose precision and generalization ability can be traded off depending on data availability. A variation on usual data mining algorithms, originally designed to mine static data, is proposed so that contextual and visual data at adaptive granularity levels can be mined. This framework was evaluated in e-ophtha , a dataset of 25,702 examination records from the OPHDIAT screening network, as well as in the publicly-available Messidor dataset. It was successfully applied to the detection of patients that should be referred to an ophthalmologist and also to the specific detection of several pathologies. [ABSTRACT FROM AUTHOR] – Name: AbstractSuppliedCopyright Label: Group: Ab Data: <i>Copyright of Medical Image Analysis is the property of Elsevier B.V. and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.) |
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| RecordInfo | BibRecord: BibEntity: Identifiers: – Type: doi Value: 10.1016/j.media.2015.12.006 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 18 StartPage: 47 Subjects: – SubjectFull: Retinal disease diagnosis Type: general – SubjectFull: Data mining Type: general – SubjectFull: Retinal diseases Type: general – SubjectFull: Automation Type: general – SubjectFull: Diabetic retinopathy Type: general – SubjectFull: Prevention Type: general Titles: – TitleFull: Automatic detection of referral patients due to retinal pathologies through data mining. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Quellec, Gwenolé – PersonEntity: Name: NameFull: Lamard, Mathieu – PersonEntity: Name: NameFull: Erginay, Ali – PersonEntity: Name: NameFull: Chabouis, Agnès – PersonEntity: Name: NameFull: Massin, Pascale – PersonEntity: Name: NameFull: Cochener, Béatrice – PersonEntity: Name: NameFull: Cazuguel, Guy IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 04 Text: Apr2016 Type: published Y: 2016 Identifiers: – Type: issn-print Value: 13618415 Numbering: – Type: volume Value: 29 Titles: – TitleFull: Medical Image Analysis Type: main |
| ResultId | 1 |
