Learning density independent texture features

Michiel Gijsbertus J. Kallenberg; Mads Nielsen; Katharina Holland; Nico Karssemeijer; Christian Igel; Martin Lillholm

doi:10.1007/978-3-319-41546-8_38

Learning density independent texture features

Michiel Gijsbertus J. Kallenberg, Mads Nielsen, Katharina Holland, Nico Karssemeijer, Christian Igel, Martin Lillholm

Datalogisk Institut

Abstract

Breast cancer risk assessment is becoming increasingly important in clinical practice. It has been suggested that features that characterize mammographic texture are more predictive for breast cancer than breast density. Yet, strong correlation between both types of features is an issue in many studies. In this work we investigate a method to generate texture features and/or scores that are independent of breast density. The method is especially useful in settings where features are learned from the data itself. We evaluate our method on a case control set comprising 394 cancers, and 1182 healthy controls. We show that the learned density independent texture features are significantly associated with breast cancer risk. As such it may aid in exploring breast characteristics that are predictive of breast cancer irrespective of breast density. Furthermore it offers opportunities to enhance personalized breast cancer screening beyond breast density.

Originalsprog	Engelsk
Titel	Breast Imaging : 13th International Workshop, IWDM 2016, Malmö, Sweden, June 19-22, 2016, Proceedings
Redaktører	Anders Tingberg, Kristina Lång, Pontus Timberg
Antal sider	8
Forlag	Springer
Publikationsdato	2016
Sider	299-306
Kapitel	38
ISBN (Trykt)	978-3-319-41545-1
ISBN (Elektronisk)	978-3-319-41546-8
DOI	https://doi.org/10.1007/978-3-319-41546-8_38
Status	Udgivet - 2016
Begivenhed	13th International Workshop on Breast Imaging - Malmö Live, Malmö, Sverige Varighed: 19 jun. 2016 → 22 jun. 2016 Konferencens nummer: 13

Konference

Konference	13th International Workshop on Breast Imaging
Nummer	13
Lokation	Malmö Live
Land/Område	Sverige
By	Malmö
Periode	19/06/2016 → 22/06/2016

Navn	Lecture notes in computer science
Vol/bind	9699
ISSN	0302-9743

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10.1007/978-3-319-41546-8_38

Citationsformater

Learning density independent texture features. / Kallenberg, Michiel Gijsbertus J.; Nielsen, Mads; Holland, Katharina et al.
Breast Imaging: 13th International Workshop, IWDM 2016, Malmö, Sweden, June 19-22, 2016, Proceedings. red. / Anders Tingberg; Kristina Lång; Pontus Timberg. Springer, 2016. s. 299-306 (Lecture notes in computer science, Bind 9699).

Publikation: Bidrag til bog/antologi/rapport › Konferencebidrag i proceedings › Forskning › peer review

Kallenberg, MGJ, Nielsen, M, Holland, K, Karssemeijer, N, Igel, C & Lillholm, M 2016, Learning density independent texture features. i A Tingberg, K Lång & P Timberg (red), Breast Imaging: 13th International Workshop, IWDM 2016, Malmö, Sweden, June 19-22, 2016, Proceedings. Springer, Lecture notes in computer science, bind 9699, s. 299-306, 13th International Workshop on Breast Imaging, Malmö, Sverige, 19/06/2016. https://doi.org/10.1007/978-3-319-41546-8_38

Kallenberg MGJ, Nielsen M, Holland K, Karssemeijer N, Igel C , Lillholm M. Learning density independent texture features. I Tingberg A, Lång K, Timberg P, red., Breast Imaging: 13th International Workshop, IWDM 2016, Malmö, Sweden, June 19-22, 2016, Proceedings. Springer. 2016. s. 299-306. (Lecture notes in computer science, Bind 9699). doi: 10.1007/978-3-319-41546-8_38

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abstract = "Breast cancer risk assessment is becoming increasingly important in clinical practice. It has been suggested that features that characterize mammographic texture are more predictive for breast cancer than breast density. Yet, strong correlation between both types of features is an issue in many studies. In this work we investigate a method to generate texture features and/or scores that are independent of breast density. The method is especially useful in settings where features are learned from the data itself. We evaluate our method on a case control set comprising 394 cancers, and 1182 healthy controls. We show that the learned density independent texture features are significantly associated with breast cancer risk. As such it may aid in exploring breast characteristics that are predictive of breast cancer irrespective of breast density. Furthermore it offers opportunities to enhance personalized breast cancer screening beyond breast density.",

author = "Kallenberg, {Michiel Gijsbertus J.} and Mads Nielsen and Katharina Holland and Nico Karssemeijer and Christian Igel and Martin Lillholm",

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doi = "10.1007/978-3-319-41546-8_38",

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AU - Lillholm, Martin

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N2 - Breast cancer risk assessment is becoming increasingly important in clinical practice. It has been suggested that features that characterize mammographic texture are more predictive for breast cancer than breast density. Yet, strong correlation between both types of features is an issue in many studies. In this work we investigate a method to generate texture features and/or scores that are independent of breast density. The method is especially useful in settings where features are learned from the data itself. We evaluate our method on a case control set comprising 394 cancers, and 1182 healthy controls. We show that the learned density independent texture features are significantly associated with breast cancer risk. As such it may aid in exploring breast characteristics that are predictive of breast cancer irrespective of breast density. Furthermore it offers opportunities to enhance personalized breast cancer screening beyond breast density.

AB - Breast cancer risk assessment is becoming increasingly important in clinical practice. It has been suggested that features that characterize mammographic texture are more predictive for breast cancer than breast density. Yet, strong correlation between both types of features is an issue in many studies. In this work we investigate a method to generate texture features and/or scores that are independent of breast density. The method is especially useful in settings where features are learned from the data itself. We evaluate our method on a case control set comprising 394 cancers, and 1182 healthy controls. We show that the learned density independent texture features are significantly associated with breast cancer risk. As such it may aid in exploring breast characteristics that are predictive of breast cancer irrespective of breast density. Furthermore it offers opportunities to enhance personalized breast cancer screening beyond breast density.

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Learning density independent texture features

Abstract

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