Improved detection of cancer in screening mammograms by temporal comparison

Fei Ma; Mariusz Bajger; Simon Williams; Murk Bottema

doi:10.1007/978-3-642-13666-5_101

Improved detection of cancer in screening mammograms by temporal comparison

Fei Ma, Mariusz Bajger, Simon Williams, Murk Bottema

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

7 Citations (Scopus)

Abstract

A method is presented for including information from the preceeding mammogram in a scheme for automatically detecting malignant masses in screening mammograms. The method circumvents the inherent difficulty of registering temporal mammograms by replacing image registration by graph matching. The scheme incorporates a single image mass detection algorithm and so the contribution of the temporal analysis can be measured. At a true detection rate of 80 percent, the single image scheme results in 1.02 false positive detections per image while the temporal scheme results in 0.96 false positives. At 90 percent true detection, the false positive rates per image are 1.84 and 1.63 respectively.

Original language	English
Title of host publication	Digital Mammography - 10th International Workshop, IWDM 2010, Proceedings
Subtitle of host publication	10th International Workshop, IWDM 2010, Girona, Catalonia, Spain, June 16-18, 2010. Proceedings
Editors	Joan Martí, Arnau Oliver, Jordi Freixenet, Robert Martí
Publisher	Springer
Pages	752-759
Number of pages	8
ISBN (Electronic)	978-3-642-13666-5
ISBN (Print)	978-3-642-13665-8
DOIs	https://doi.org/10.1007/978-3-642-13666-5_101
Publication status	Published - 21 Jul 2010
Event	International Workshop on Digital Mammography - Catalonia, Spain Duration: 16 Jun 2010 → 18 Jun 2010 Conference number: 10th

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	6136 LNCS
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Conference

Conference	International Workshop on Digital Mammography
Country/Territory	Spain
City	Catalonia
Period	16/06/10 → 18/06/10

Keywords

breast cancer
computer-aided mammography
graph matching
mass detection
temporal analysis

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

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https://rdcu.be/dA8GtLicence: In Copyright

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Ma, F., Bajger, M., Williams, S., & Bottema, M. (2010). Improved detection of cancer in screening mammograms by temporal comparison. In J. Martí, A. Oliver, J. Freixenet, & R. Martí (Eds.), Digital Mammography - 10th International Workshop, IWDM 2010, Proceedings: 10th International Workshop, IWDM 2010, Girona, Catalonia, Spain, June 16-18, 2010. Proceedings (pp. 752-759). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 6136 LNCS). Springer . https://doi.org/10.1007/978-3-642-13666-5_101

Ma, Fei ; Bajger, Mariusz ; Williams, Simon et al. / Improved detection of cancer in screening mammograms by temporal comparison. Digital Mammography - 10th International Workshop, IWDM 2010, Proceedings: 10th International Workshop, IWDM 2010, Girona, Catalonia, Spain, June 16-18, 2010. Proceedings. editor / Joan Martí ; Arnau Oliver ; Jordi Freixenet ; Robert Martí. Springer , 2010. pp. 752-759 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

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title = "Improved detection of cancer in screening mammograms by temporal comparison",

abstract = "A method is presented for including information from the preceeding mammogram in a scheme for automatically detecting malignant masses in screening mammograms. The method circumvents the inherent difficulty of registering temporal mammograms by replacing image registration by graph matching. The scheme incorporates a single image mass detection algorithm and so the contribution of the temporal analysis can be measured. At a true detection rate of 80 percent, the single image scheme results in 1.02 false positive detections per image while the temporal scheme results in 0.96 false positives. At 90 percent true detection, the false positive rates per image are 1.84 and 1.63 respectively.",

keywords = "breast cancer, computer-aided mammography, graph matching, mass detection, temporal analysis",

author = "Fei Ma and Mariusz Bajger and Simon Williams and Murk Bottema",

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Ma, F, Bajger, M, Williams, S & Bottema, M 2010, Improved detection of cancer in screening mammograms by temporal comparison. in J Martí, A Oliver, J Freixenet & R Martí (eds), Digital Mammography - 10th International Workshop, IWDM 2010, Proceedings: 10th International Workshop, IWDM 2010, Girona, Catalonia, Spain, June 16-18, 2010. Proceedings. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 6136 LNCS, Springer , pp. 752-759, International Workshop on Digital Mammography, Catalonia, Spain, 16/06/10. https://doi.org/10.1007/978-3-642-13666-5_101

Improved detection of cancer in screening mammograms by temporal comparison. / Ma, Fei; Bajger, Mariusz; Williams, Simon et al.
Digital Mammography - 10th International Workshop, IWDM 2010, Proceedings: 10th International Workshop, IWDM 2010, Girona, Catalonia, Spain, June 16-18, 2010. Proceedings. ed. / Joan Martí; Arnau Oliver; Jordi Freixenet; Robert Martí. Springer , 2010. p. 752-759 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 6136 LNCS).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

TY - GEN

T1 - Improved detection of cancer in screening mammograms by temporal comparison

AU - Ma, Fei

AU - Bajger, Mariusz

AU - Williams, Simon

AU - Bottema, Murk

N1 - Conference code: 10th

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N2 - A method is presented for including information from the preceeding mammogram in a scheme for automatically detecting malignant masses in screening mammograms. The method circumvents the inherent difficulty of registering temporal mammograms by replacing image registration by graph matching. The scheme incorporates a single image mass detection algorithm and so the contribution of the temporal analysis can be measured. At a true detection rate of 80 percent, the single image scheme results in 1.02 false positive detections per image while the temporal scheme results in 0.96 false positives. At 90 percent true detection, the false positive rates per image are 1.84 and 1.63 respectively.

AB - A method is presented for including information from the preceeding mammogram in a scheme for automatically detecting malignant masses in screening mammograms. The method circumvents the inherent difficulty of registering temporal mammograms by replacing image registration by graph matching. The scheme incorporates a single image mass detection algorithm and so the contribution of the temporal analysis can be measured. At a true detection rate of 80 percent, the single image scheme results in 1.02 false positive detections per image while the temporal scheme results in 0.96 false positives. At 90 percent true detection, the false positive rates per image are 1.84 and 1.63 respectively.

KW - breast cancer

KW - computer-aided mammography

KW - graph matching

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SN - 978-3-642-13665-8

T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

SP - 752

EP - 759

BT - Digital Mammography - 10th International Workshop, IWDM 2010, Proceedings

A2 - Martí, Joan

A2 - Oliver, Arnau

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PB - Springer

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Y2 - 16 June 2010 through 18 June 2010

ER -

Ma F, Bajger M, Williams S, Bottema M. Improved detection of cancer in screening mammograms by temporal comparison. In Martí J, Oliver A, Freixenet J, Martí R, editors, Digital Mammography - 10th International Workshop, IWDM 2010, Proceedings: 10th International Workshop, IWDM 2010, Girona, Catalonia, Spain, June 16-18, 2010. Proceedings. Springer . 2010. p. 752-759. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). doi: 10.1007/978-3-642-13666-5_101

Improved detection of cancer in screening mammograms by temporal comparison

Abstract

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Keywords

UN SDGs

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